Serum α-1 acid glycoprotein as a potential biomarker is associated with sarcopenia among American women aged 20-49: A cross-sectional study

Serum α-1 acid glycoprotein as a potential biomarker is associated with sarcopenia among American women aged 20-49: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Serum α-1 acid glycoprotein as a potential biomarker is associated with sarcopenia among American women aged 20-49: A cross-sectional study Shuo Zhang, Jiaxuan Chen, Chunke Dong, Di Wu, Haoyun Zheng, Yonggang Zhu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6743872/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The role of α-1 acid glycoprotein (AGP) as an inflammatory marker in sarcopenia remains unclear. This study, concentrating on young women, investigated the potential of serum AGP as a biomarker for stratifying the risk of sarcopenia. Methods Utilizing nationally representative data from 1,417 women (20–49 years) in the National Health and Nutrition Examination Survey, we conducted a multivariable-adjusted analysis through weighted logistic regression models. Stratified analyses incorporated interaction testing across clinically relevant subgroups. Nonlinear associations were interrogated using restricted cubic spline modeling with threshold detection algorithms, and sensitivity analyses were performed. Results In the fully adjusted model, each 1-unit rise in AGP concentration increased sarcopenia risk by 2% (OR 1.02, 95% CI 1.01–1.03). This link was more evident in individuals aged 30–39, without diabetes, with hypoalbuminemia, and CRP levels below 1.05 mg/L. Additionally, RCS and threshold analysis demonstrated a nonlinear relationship, identifying a turning point at 76.3 mg/dL. Conclusion Our findings corroborate the association between AGP levels and the risk of sarcopenia in women aged 20 to 49 years. This positions AGP quantification as a promising biomarker for early detection and risk stratification of sarcopenia. α-1 acid glycoprotein sarcopenia inflammation NHANES biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sarcopenia, a progressive skeletal muscle disorder characterized by loss of muscle mass, strength, and function, has emerged as a critical public health challenge [ 1 , 2 ]. While traditionally viewed as a geriatric condition, accumulating evidence highlights its early onset in middle-aged populations, with women exhibiting twice the prevalence compared to men [ 3 , 4 ]. This gender disparity underscores the urgency to identify early biomarkers and modifiable risk factors, particularly in younger cohorts, to mitigate downstream consequences such as physical disability, metabolic dysfunction, and increased mortality [ 5 , 6 ]. Current diagnostic reliance on imaging modalities such as DXA and muscle strength measurements limits early detection. This emphasizes the need for accessible biomarkers to stratify risk and guide interventions, which are simpler and more cost-effective [ 7 ]. The pathophysiology of sarcopenia remains multifactorial, involving intertwined mechanisms of chronic inflammation, oxidative stress, and metabolic dysregulation. Among these, low-grade systemic inflammation has gained prominence as a key driver of muscle catabolism [ 8 , 9 ]. Pro-inflammatory cytokines, including interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), contribute to impaired muscle protein synthesis and increased insulin resistance [ 10 ]. However, the role of acute-phase proteins, which bridge inflammation and metabolic pathways, remains underexplored [ 11 ]. Given the intricate pathophysiological mechanisms underlying sarcopenia, it is probable that specific panels of biomarkers are required to effectively capture the onset and progression of sarcopenia through multiple pathways [ 2 ]. Considering the inflammation-mediated pathway, we focused on α-1 acid glycoprotein. α-1 acid glycoprotein (AGP), produced by hepatocytes, is a sensitive indicator of systemic inflammation and immune activation. AGP can hinder insulin-driven glucose oxidation in skeletal muscle. It also has anti-fatigue properties, activates AMPK by binding to the CCR5 receptor, and boosts muscle glycogen synthesis and endurance [ 12 – 14 ]. Despite these mechanistic insights, epidemiological data linking AGP to sarcopenia are scarce, particularly in non-elderly populations. Notably, existing studies on sarcopenia biomarkers, such as CRP and IL-6, have yielded inconsistent associations, highlighting the complexity of inflammatory pathways in muscle health [ 6 , 10 , 15 – 21 ]. AGP, with its unique dual role in regulating inflammation and energy metabolism, emerges as a compelling biomarker candidate for sarcopenia in young and middle-aged women. Previous classic studies have indicated that serum AGP concentration levels tend to increase with age in the general population, with a more pronounced effect observed in women [ 22 ]. While prior studies have established its diagnostic utility in distinguishing metabolically healthy women from those with metabolic syndrome (MetS), particularly in overweight/obese populations [ 23 ], this investigation pioneers the exploration of AGP's relationship with sarcopenia in women. Addressing this gap is critical, as early identification of at-risk individuals could enable targeted anti-inflammatory or nutritional strategies to delay disease progression [ 24 ]. This population-based cross-sectional analysis employed data from the nationally representative NHANES dataset to investigate the association between circulating AGP concentrations and sarcopenia risk among women aged 20–49. By focusing on a younger demographic, this work challenges the conventional geriatric framework of sarcopenia and provides novel insights into AGP’s potential as a biomarker for early muscle decline. Materials and methods Study population Data from the National Health and Nutrition Examination Survey (NHANES) was utilized for this study. This survey provides an overview of the health and nutritional status of U.S. adults and children. A complex, stratified multistage design was implemented to ensure the cohort's representativeness. NHANES data includes demographic, medical history, and some laboratory tests and physiological measurement, which are publicly available. The NCHS Research Ethics Review Committee approved the NHANES research protocol. Detailed methodological specifications for NHANES, including sampling protocols, biomarker assays, and data collection procedures, are publicly accessible through the CDC's official repository ( https://www.cdc.gov/nchs/nhanes/ ), ensuring transparency and reproducibility of this secondary analysis. We used data from two NHANES two-year cycles (2015–2016, and 2017–2018) involving 19,226 participants for analysis in this study. Initially, we excluded all male participants, along with female participants whose ages fell outside the 20 to 49 age range. Next, participants lacking data on DXA and BMI were excluded, which determine the definition of sarcopenia. Then we excluded those missing AGP data. In addition, those with other incomplete data were excluded. The final analytical cohort comprised 1,417 eligible participants (Fig. 1 ). Measurement of AGP NHANES employed the Tina-quant Roche AAGP2 assay to assess α-1-acid glycoprotein (AGP). The principle of immunological agglutination is the basis of this assay. This method forms an antigen-antibody complex through the interaction of anti-AGP antibodies with sample antigens, leading to agglutination. The extent of agglutination is assessed using turbidimetry (AAGP2 Tina-quant α1-Acid Glycoprotein Gen.2 [package insert]). The laboratory and method were certified under the 1988 Clinical Laboratory Improvement Amendment. Each run involved duplicate analyses of either in-house serum QC pools at three levels or Roche QC pools at two levels, with validity assessed through a multi-rule QC program. Definition of sarcopenia The European Working Group on Sarcopenia in Older People 2 (EWGSOP2) guidelines indicate that, while contemporary understanding suggests that low muscle strength supersedes low muscle mass as the primary determinant of sarcopenia, the diagnosis of sarcopenia is ultimately established through the identification of diminished muscle quantity or quality [ 2 ]. Appendicular skeletal muscle mass (ASM; summing upper and lower extremity muscle mass) was quantified via dual-energy x-ray absorptiometry. Sarcopenia diagnosis utilized the ASM/BMI ratio, with sex-specific thresholds (women < 0.512; men < 0.789) [ 7 ]. Covariates Our study included covariates to assess the potential impact on the AGP-sarcopenia relationship. Demographic data were gathered, including age (categorized into 20–29, 30–39, and 40–49), race and ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), education level (less than 9th grade, 9-11th grade, high school graduate, some college or AA degree, and college graduate or above), and family poverty-income ratio (PIR) categories (1.3, 1.3–1.8, 1.8). The biochemical parameters assessed were total cholesterol (mmol/L), direct HDL-cholesterol (mmol/L), 25-hydroxyvitamin D2 + D3 (nmol/L), glycohemoglobin (%), serum albumin (g/L), and CRP (mg/L) [ 25 , 26 ]. Hypoalbuminemia is characterized by a serum albumin level below 35 g/L. Furthermore, the Physical Activity Guidelines for Americans suggest over 75 minutes of vigorous or 150 minutes of moderate exercise weekly [ 27 ]. We classified participants' activity levels using questionnaire data into active (meeting or exceeding guidelines), less active (below guidelines), and inactive (no activity). Body mass index (BMI) was categorized as under/normal weight ( 28.0 kg/m2). Diabetes diagnosis was based on glycohemoglobin levels ≥ 6.5% and patient self-reports. Statistical analysis Participants were stratified by sarcopenia status for analytical comparisons. Continuous measures were expressed as weighted means ± SD, with group differences assessed through nonparametric comparative analysis (Kruskal-Wallis test). Categorical variables were reported as weighted proportions (95% CI) and analyzed using weighted Rao-Scott χ² tests with Fisher's exact verification. Multicollinearity was systematically evaluated through variance inflation factors (VIF < 10 criterion). Multivariable logistic regression with sequential adjustment strategy elucidated AGP-sarcopenia associations: Model 1 was unadjusted. Model 2 was adjusted for demographic data (age, race and ethnicity, education level, PIR). Model 3 was fully adjusted for behavioral, metabolic, and biochemical covariates (physical activity, diabetes, hypoalbuminemia, serum biomarkers). Exposure was parameterized as ordinal tertile variables (reference: first tertile) for trend analysis. Nonlinear dynamics were interrogated through weighted restricted cubic spline (RCS) regression with threshold detection algorithms. Additionally, subgroup analyses were conducted by examining age groups (20–29/30–39/40–49), physical activity (inactive/ less active/ active), CRP groups (4.00 mg/L), diabetes (yes/no), and hypoalbuminemia (yes/no). We incorporated interaction tests to evaluate the associations between various subgroups and exposure factors. Interaction testing was performed by incorporating multiplicative terms (e.g., AGP × age, AGP × CRP) into fully adjusted logistic regression models. P-values for interaction were derived using Wald tests. NHANES employs a complex, multistage probability sampling design. To account for oversampling, nonresponse, and population representation, survey weights (WTMEC2YR) were multiplied by 2/4, as recommended by NHANES guidelines for combining multiple cycles. Analyses were conducted using Empower Stats (v2.0; X&Y Solutions Inc), and STATA (v17.0). A p-value below 0.05 was deemed statistically significant. Results Baseline characteristics of study participants The study involved 1,417 female participants, averaging 34.4 years of age with a standard deviation of 8.7 years. Among these women, 6.8% were identified as having sarcopenia. Ulteriorly, the incidence in participants aged 20–29 was 4.8%, while 5.8% in 30–39, and 10% in 40–49. Women with sarcopenia were more likely to be Mexican-American or other Hispanic and to have lower education (below high school) and income levels (PIR ≤ 1.8) compared to those without the condition. Women with sarcopenia tended to be obese (BMI > 28 kg/m², 37.0 ± 9.3), exhibit hypoalbuminemia, and engage in low levels of physical activity. Furthermore, this cohort exhibited a marked increase in serum AGP and CRP levels, coupled with a relative decrease in HDL-C and 25-hydroxyvitamin D2 + D3 concentrations, compared to individuals without sarcopenia. Table 1 presents a comprehensive overview of the baseline characteristics. Table 1 Characteristics of participants in the NHANES 2015–2018 cycles Variable Total Sarcopenia P value No Yes Overall, n (%) 1417 1320(93.2) 97(6.8) Age, years, mean ± SD 34.4 ± 8.7 34.3 ± 8.7 36.1 ± 8.4 0.0862 Age 0.0813 20–29 34.7(31.6, 37.9) 35.1(31.9, 38.4) 27.3(17.8, 39.5) 30–39 32.7(29.7, 35.9) 33.0(29.8, 36.3) 28.4(18.9, 40.3) 40–49 32.6(29.4, 36) 31.9(28.6, 35.5) 44.3(32.7, 56.5) Race and ethnicity < 0.0001 Mexican American 11.2(9.8, 12.8) 9.8(8.5, 11.4) 36.3(26.4, 47.5) Other Hispanic 7.3(6.1, 8.7) 7.2(6.0, 8.6) 9.4(5.2, 16.4) Non-Hispanic White 61.8(58.8, 64.7) 62.7(59.7, 65.7) 44.1(32.1, 56.8) Non-Hispanic Black 9.0(7.8, 10.3) 9.4(8.1, 10.8) 1.8(0.6, 5.6) Other races 10.7(9.2, 12.5) 10.9(9.2, 12.7) 8.4(3.6, 18.3) Education level < 0.0001 Less than 9th grade 3.0(2.3, 3.9) 2.8(2.1, 3.7) 6.6(3.6, 11.9) 9-11th grade 6.0(4.8, 7.4) 5.3(4.2, 6.6) 19.3(11.8, 29.9) High school graduate 18.3(16.0, 20.8) 18.1(15.7, 20.8) 20.9(12.8, 32.1) Some college or AA degree 36.4(33.3, 39.6) 36.4(33.2, 39.7) 36.0(25.2, 48.5) College graduate or above 36.4(33.0, 39.9) 37.4(33.9, 41.1) 17.2(9.1, 30.0) PIR < 0.0001 1.8 65.3(62.3, 68.2) 66.7(63.6, 69.6) 41.8(30.1, 54.5) BMI < 0.0001 28 47.0(43.6, 50.3) 44.8(41.4, 48.3) 85.8(75.5, 92.2) Diabetes 0.9121 NO 96.2(95, 97.2) 96.2(94.9, 97.2) 96.0(91, 98.3) YES 3.8(2.8, 5.0) 3.8(2.8, 5.1) 4(1.7, 9.0) Physical activities 0.0007 Inactive 21.1(18.6, 23.8) 20.1(17.6, 22.9) 38.6(27.9, 50.6) Less active 10.9(8.9, 13.3) 11.0(8.9, 13.6) 8.3(4.4, 15.1) Active 68(64.8, 71.0) 68.8(65.5, 71.9) 53.1(41.2, 64.7) Hypoproteinemia < 0.0001 NO 96.6(95.2, 97.6) 97.1(95.7, 98.1) 87.9(76, 94.3) YES 3.4(2.4, 4.8) 2.9(1.9, 4.3) 12.1(5.7, 24.0) BMI (kg/m 2 ) 29.1 ± 7.7 28.6 ± 7.4 37.0 ± 9.3 < 0.0001 Albumin (g/L) 41.8 ± 3.3 41.9 ± 3.3 40.2 ± 3.6 < 0.0001 HDL-Cholesterol (mmol/L) 1.5 ± 0.4 1.5 ± 0.4 1.3 ± 0.3 < 0.0001 Total Cholesterol (mmol/L) 4.7 ± 0.9 4.7 ± 0.9 4.8 ± 0.8 0.758 Vitamin D (nmol/L) 67.3 ± 26.3 67.9 ± 26.5 56.7 ± 20.3 0.0003 Glycohemoglobin (%) 5.4 ± 0.7 5.4 ± 0.7 5.5 ± 0.7 0.2052 C-Reactive Protein (mg/L) 4.3 ± 7.1 4.0 ± 6.2 10.0 ± 15.6 < 0.0001 AGP (mg/dL) 79.9 ± 24.2 79.0 ± 23.6 95.9 ± 28.8 < 0.0001 NHANES, National Health and Nutrition Examination Survey; SD, standard deviation; PIR, poverty income ratio; BMI, body mass index (calculated as kilograms divided by meters squared); Vitamin D: 25-hydroxyvitamin D2 and D3; Hypoproteinemia was defined as Albumin(g/L) < 35g/L; Diabetes was defined as an HbA1c level ≥ 6.5%, or an FPG level ≥ 7.0 mmol/L, or a self-reported diagnosis of diabetes. The association between AGP and sarcopenia Prior to conducting the analyses, we assessed collinearity among the covariates. The VIF for all covariates was found to be less than 10, suggesting the absence of multicollinearity. Firstly, AGP was conventionally treated as a continuous variable. The study identified a significant positive correlation between AGP levels and sarcopenia incidence, irrespective of confounder adjustments. A one-unit increase in AGP is associated with a 3% higher risk of developing sarcopenia in both model 1 (OR 1.03, 95% CI 1.02–1.03, P < 0.001) and model 2 (OR 1.03, 95% CI 1.02–1.04, P < 0.001), and a 2% increase in model 3 (OR 1.02, 95% CI 1.01–1.03, P < 0.001). A one-SD increase in AGP corresponds to higher risk of sarcopenia. Meanwhile, when AGP was converted into a categorical variable by tertile, the study showed analogously positive results. Individuals in the highest AGP tertile exhibited a significantly increased risk of sarcopenia compared to those in the lowest tertile across all models: model 1 (OR 4.87, 95% CI 2.41–9.86; P for trend < 0.001), model 2 (OR 4.72, 95% CI 2.31–9.66; P for trend < 0.001), and model 3 (OR 3.42, 95% CI 1.50–7.77; P for trend = 0.003). (Table 2 ) Table 2 Associations of the AGP with the risk of Sarcopenia AGP (mg/dL) Model 1 OR, (95% CI), P Model 2 OR, (95% CI), P Model 3 OR, (95% CI), P Per 1 unit increase 1.03(1.02, 1.03) < 0.001 1.03(1.02,1.04) < 0.001 1.02(1.01, 1.03) < 0.001 Per 1 SD increase 1.80(1.45, 2.24) < 0.001 1.89(1.48, 2.42) < 0.001 1.44(1.08, 1.93) 0.014 AGP Tertile 1 (ref) 1.00 1.00 1.00 AGP Tertile 2 3.03(1.45, 6.31) 0.003 2.68(1.24, 5.79) 0.012 2.21(0.95, 5.13) 0.066 AGP Tertile 3 4.87(2.41, 9.86) < 0.001 4.72(2.31, 9.66) < 0.001 3.42(1.50, 7.77) 0.003 P-trend <0.001 <0.001 0.003 OR, Odds Ratio; Cl, Confidence Interval; ref, Reference; Model 1: Non-adjusted model; Model 2 adjusted for: Age; Race and ethnicity; Education level; PIR; Model 3 adjusted for: Age; Race and ethnicity; Education level; PIR; Diabetes; Physical activities; Albumin(g/L); CRP(mg/L); Direct HDL-Cholesterol(mmol/L); Total Cholesterol(mmol/L); Vitamin D(nmol/L); Glycohemoglobin (%). Subgroup analysis and interaction test Subsequent analysis utilized the fully adjusted model (model 3). For more comprehensive results, CRP was grouped by tertile. Subgroup analyses were performed considering age, physical activity, CRP levels, Vitamin D levels, hypoalbuminemia status, and diabetes status. Serum AGP levels showed significant positive correlations with sarcopenia in participants aged 30–39 years (P = 0.010), those with hypoalbuminemia (P = 0.041), non-diabetic individuals (P = 0.016), and those with CRP levels below 1.05 mg/L (P = 0.004). Beyond that, interaction tests showed obvious joint effect between CRP and AGP (Interaction P = 0.018). Figure 2 illustrates the findings. Association between AGP and sarcopenia, each stratification was adjusted for Age; Race and ethnicity; Education level; PIR; Diabetes; Physical activities; Hypoalbuminemia; Albumin(g/L); CRP(mg/L); Direct HDL-Cholesterol(mmol/L); Total Cholesterol(mmol/L); Vitamin D (nmol/L); Glycohemoglobin (%); Squares indicate odds ratios (ORs), with horizontal lines indicating 95% Cl. Nonlinear association between AGP and sarcopenia Otherwise, as indicated by RCS, the relationship between the AGP level and sarcopenia was nonlinear (Fig. 3 ). We performed a sensitivity analysis by transforming the adjusted models, which verified the robustness of the findings (Fig. 4 ). This complex association was parametrically analyzed through piecewise regression modeling. The AGP level's inflection point was identified at 76.3. A positive correlation was identified when serum AGP concentration was under 76.3 (OR 1.09, 95% CI 1.03–1.15, P = 0.003). A slightly weaker positive correlation was observed when the concentration surpassed 76.3 (OR 1.02, 95% CI 1.00–1.04, P = 0.048). (Table 3 ) a: analysis of restricted cubic spline sarcopenia using the fully adjusted model (Model 3), as shown in Fig. 1 in the main text; b: RCS using the unadjusted model (Model 1); c: RCS using the minimally adjusted (Model 2). Table 3 Threshold effect analysis Outcomes Breakpoint. OR (95%Cl) P value Inflection point (K) 76.3 OR1, (95% CI) ( K) 1.02(1.00, 1.04) 0.048 Likelihood Ratio test 0.048 Discussion Utilizing NHANES 2015–2018 data, this cross-sectional study offers new insights into the relationship between serum α-1 acid glycoprotein (AGP) and sarcopenia in American women aged 20–49 years. The study determined that AGP levels are associated with the risk of sarcopenia in young women, exhibiting a nonlinear threshold effect at a cutoff point of 76.3 mg/dL. These results not only align with emerging evidence on the role of chronic inflammation in muscle degeneration but also extend current knowledge by highlighting AGP’s unique position linking inflammatory and metabolic pathways in younger female populations. Below, we contextualize these findings within existing literature, explore potential mechanisms, and address implications for research and clinical practice. Previous research on neonatal swine has demonstrated a correlation between elevated levels of AGP and both impaired growth and diminished muscle mass [ 28 , 29 ]. Our study's findings align with these observations. The observed association between AGP and sarcopenia corroborates preclinical studies implicating chronic low-grade inflammation in muscle catabolism. AGP, an acute-phase reactant, is produced in response to pro-inflammatory cytokines like IL-6 and TNF-α, which impair muscle protein synthesis and induce insulin resistance [ 30 , 31 ]. Our findings suggest that AGP may exacerbate sarcopenia through dual mechanisms: 1) directly diminishing insulin-stimulated glucose oxidation, and specifically reducing protein synthesis, as demonstrated in experimental models of mouse C2C12 myotubes [ 13 ], and 2) amplifying systemic inflammation, thereby accelerating muscle atrophy. It has been shown that, increased AGP and decreased albumin during inflammation can cause muscle atrophy by boosting proteolysis and hindering synthesis [ 32 ]. Notably, the nonlinear relationship—characterized by a stronger association below the inflection point (76.3 mg/dL)—hints at a saturation effect or a shift in AGP’s biological role at higher concentrations. At lower levels (< 76.3 mg/dL), AGP may serve as a sensitive predictor of sarcopenia risk. Beyond this threshold, its predictive utility diminishes, potentially due to compensatory mechanisms (e.g., anti-inflammatory pathways) or confounding factors like chronic inflammation. For instance, elevated AGP levels might transition from pro-inflammatory to immunoregulatory functions [ 33 ], attenuating its direct impact on muscle loss. This hypothesis aligns with AGP’s known ability to modulate immune cell activity [ 34 ] and suppress neutrophil adhesion at higher thresholds [ 35 ], a mechanism that warrants further investigation. The interaction between AGP and CRP further underscores the complexity of inflammatory pathways in sarcopenia. While both biomarkers are acute-phase reactants, their divergent associations with muscle health highlight the multifaceted nature of "inflammaging"—the age-related chronic inflammation implicated in sarcopenia [ 36 , 37 ]. Intriguingly, our subgroup analysis revealed that AGP’s association with sarcopenia persisted even in individuals with low CRP levels (< 1.05 mg/L). This suggests that AGP may capture distinct inflammatory pathways, such as cytokine-driven hepatic synthesis or tissue-specific immune activation, which are not fully reflected by CRP. Furthermore, the synergistic interaction between AGP and CRP (P = 0.018) supports the hypothesis that cumulative inflammatory insults, rather than isolated markers, drive muscle decline [ 38 ]. These findings advocate for a multi-marker approach to stratify sarcopenia risk, particularly in populations where traditional biomarkers like CRP exhibit limited sensitivity. The stronger association between AGP and sarcopenia in women aged 30–39 years challenges the conventional view of sarcopenia as a geriatric condition. This age-specific trend may reflect early inflammatory dysregulation in midlife, potentially triggered by hormonal changes during perimenopause, sedentary lifestyles, or subclinical metabolic disturbances [ 39 – 41 ]. Nonetheless, AGP is also identified as an endogenous anti-fatigue protein. An experimental study has indicated that women exhibit lower muscular endurance compared to men, potentially due to estrogen's down-regulation of AGP levels [ 42 ]. In conjunction with our findings, this suggests that AGP may possess a complex regulatory role in skeletal muscle metabolism. Mitochondrial dysfunction, a hallmark of aging, may also play a role: impaired energy metabolism in muscle tissue could exacerbate oxidative stress, creating a pro-inflammatory milieu that upregulates AGP synthesis [ 43 , 44 ]. In a study aimed at elucidating sex differences in the mechanisms underlying mouse sarcopenia, it was observed that males exhibited diminished mitochondrial biogenesis, oxidative capacity, and AMPK-autophagy signaling during the progression of sarcopenia. In contrast, these attributes were not detected in females [ 45 ]. Importantly, our results emphasize the need for early monitoring of inflammatory markers in younger at-risk populations, as interventions during this critical window—such as anti-inflammatory therapies or lifestyle modifications—may delay or mitigate muscle loss before irreversible damage occurs. Elsewhere, a cross-sectional study focusing on oldest-old men have suggested that AGP indirectly affects D3-creatine dilution muscle mass through the kynurenine pathway metabolites [ 46 ]. This also provides a theoretical basis for extending the research on AGP and sarcopenia to the whole population in the future. The heightened sarcopenia risk observed in women with hypoalbuminemia (serum albumin < 35 g/L) underscores the interplay between nutritional status and inflammation. Albumin, a negative acute-phase protein, declines during systemic inflammation, while AGP, a positive acute-phase reactant, rises. This inverse relationship suggests that hypoalbuminemia may amplify AGP’s detrimental effects on muscle protein metabolism. For instance, low albumin levels could reduce oncotic pressure, exacerbating muscle edema [ 47 ] and impairing nutrient delivery to skeletal muscle [ 48 ]. Conversely, AGP’s binding affinity for drugs and hormones might disrupt anabolic signaling pathways, further compromising muscle maintenance. The study underscores the need for comprehensive approaches that tackle both inflammation and malnutrition in preventing sarcopenia, such as the concurrent use of whey protein supplements to enhance albumin production and anti-inflammatory agents aimed at AGP [ 49 ]. Recent advances in the gut-muscle axis provide additional context for our findings. Gut microbiota dysbiosis is associated with systemic inflammation and muscle atrophy through mechanisms involving bacterial lipopolysaccharides (LPS), short-chain fatty acids (SCFAs), and immune cell activation [ 50 ]. AGP, as a modulator of intestinal barrier function, may indirectly influence muscle health by regulating gut permeability and microbial translocation [ 51 ]. Preclinical studies suggest that probiotics and prebiotics can attenuate muscle loss by reducing circulating LPS and pro-inflammatory cytokines, thereby suppressing AGP synthesis [ 50 , 52 ]. Future research should explore whether AGP serves as a mediator in the gut-muscle axis, bridging microbial metabolites to inflammatory pathways in sarcopenia. Based on the above discussion, this study has the value of clinical transformation. Clinically, AGP assays are already available in routine blood panels (immunology profiles) at costs comparable to CRP in many healthcare systems. However, standardization of AGP thresholds for sarcopenia remains lacking, and its adoption may require validation in diverse populations. Our findings highlight the clinical relevance of "within-normal-range variations", consistent with emerging concepts of "subclinical biomarker". If validated longitudinally, AGP measurement could complement existing tools for early sarcopenia detection, particularly in high-risk subgroups such as women with hypoalbuminemia, low CRP levels, or aged 30–39 years. By enabling risk stratification during the subclinical phase, AGP may facilitate timely interventions—such as resistance training, dietary modifications, or anti-inflammatory therapies—to mitigate muscle loss before irreversible decline. Future research should prioritize AGP’s integration into multi-marker panels and explore its utility in diverse populations and clinical settings. Strengths and limitations While this study offers valuable insights, several limitations must be acknowledged. As a cross-sectional study, causality cannot be inferred. and the focus on young/middle-aged women limits generalizability to older adults or males. Longitudinal studies are needed to validate AGP’s predictive value and elucidate its temporal relationship with muscle decline. Mechanistic research should investigate AGP’s direct effects on myocyte apoptosis, satellite cell activation, and mitochondrial function. Clinically, interventions targeting AGP-related pathways—such as IL-6 inhibitors, nutritional strategies to correct hypoalbuminemia, or gut microbiota modulation—warrant exploration in randomized trials. Conclusion In summary, this study establishes AGP as a promising biomarker for sarcopenia, particularly in younger women, and underscores the interplay between inflammation, metabolism, and nutrition in muscle health. By identifying a nonlinear threshold and age-specific associations, our findings advocate for personalized approaches to sarcopenia risk stratification and intervention. Future research should build on these results to unravel AGP’s mechanistic role and translate these insights into targeted therapies for at-risk populations. Declarations Author Contributions SZ: Visualization, Conceptualization, Data curation, Formal Analysis, Software and Writing - original draft. JC: Conceptualization, Data curation, Formal Analysis, Methodology and Writing - original draft, review & editing. SZ and JC contributed equally to this work and share first authorship. CD: conceptualization. WL: Methodology. HZ: Data curation. DW: Data curation. YZ: Software. SZ (Sihao Zhao): Visualization. HW: Conceptualization, Supervision, Validation and Writing - review & editing. QL: Conceptualization, Supervision, Validation, Project administration, Funding acquisition, and Writing - review & editing. All authors participated in the development of this article and have given their approval for the submitted version. Funding This study was supported by the Capital Clinical Characteristic Diagnosis and Treatment Technology Research and Translational Application Project (Z221100007422122). Availability of data and material The NHANES datasets for this study can be found in the CDC's official repository (https://www.cdc.gov/nchs/nhanes/). Acknowledgments We sincerely thank the participants and staff of NHANES for their valuable work and dedication. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Consent to participate was obtained and the National Center for Health Statistics (NCHS) ethics committee approved NHANES study protocol. Study protocols for NHANES were approved by the NCHS ethnics review board (Protocol #2011-17). The participants provided their written informed consent before taking part in this study. All information from the NHANES program is available and free for the public, so the agreement of the medical ethics committee board was not necessary. Consent for publication Not applicable. Competing Interests The authors affirm that the research was conducted without any commercial or financial affiliations that could be interpreted as potential conflicts of interest. References Sayer AA, Cruz-Jentoft A. Sarcopenia definition, diagnosis and treatment: consensus is growing. Age Ageing. 2022;51(10):afac220. 10.1093/ageing/afac220 . Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T et al. Sarcopenia: revised European consensus on definition and diagnosis [published correction appears in Age Ageing. 2019;48(4):601. 10.1093/ageing/afz046.] . Age Ageing. 2019;48(1):16–31. doi:10.1093/ageing/afy169. Petermann-Rocha F, Balntzi V, Gray SR, Lara J, Ho FK, Pell JP, et al. 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Front Immunol. 2025;16:1467308. 10.3389/fimmu.2025.1467308 . Erdoğan K, Kara M, Şener FE, Durmuş ME, Durmuşoğlu BNÇ, Abdulsalam AJ, et al. Serum albumin as a biomarker of (nutritional status in) sarcopenia. J Bone Min Metab. 2024. 10.1007/s00774-024-01557-9 . Carboo JA, Malan L, Lombard M, Nienaber A, Dolman-Macleod RC. The relationship between 25-hydroxyvitamin D and markers of intestinal and systemic inflammation in undernourished and non-undernourished children, 6–59 months. Cytokine. 2025;185:156807. 10.1016/j.cyto.2024.156807 . Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, et al. The Physical Activity Guidelines for Americans. JAMA. 2018;320(19):2020–8. 10.1001/jama.2018.14854 . Mitchell AD, Ramsay TG, Caperna TJ, Scholz AM. Body composition of piglets exhibiting different growth rates. Arch Anim Breed. 2012;55(4):356–63. https://doi.org/10.5194/aab-55-356-2012 . Bauer R, Walter B, Hoppe A, Gaser E, Lampe V, Kauf E, et al. Body weight distribution and organ size in newborn swine (sus scrofa domestica) -- a study describing an animal model for asymmetrical intrauterine growth retardation. Experimental toxicologic pathology: official J Gesellschaft fur Toxikologische Pathologie. 1998;50(1):59–65. 10.1016/S0940-2993(98)80071-7 . Smith SA, Waters NJ. Pharmacokinetic and Pharmacodynamic Considerations for Drugs Binding to Alpha-1-Acid Glycoprotein. Pharm Res. 2018;36(2):30. 10.1007/s11095-018-2551-x . Pellegrinelli V, Rouault C, Rodriguez-Cuenca S, Albert V, Edom-Vovard F, Vidal-Puig A, et al. Human Adipocytes Induce Inflammation and Atrophy in Muscle Cells During Obesity. Diabetes. 2015;64(9):3121–34. 10.2337/db14-0796 . Zarrabian S, Attaix D, Marcy J, Biou D, Mendy F, Roger L, et al. Effects of alimentary whole proteins versus their small peptide hydrolysates on liver and skeletal muscle during the acute inflammation phase in the rat. Clin Nutr. 1998;17(4):169–76. 10.1016/s0261-5614(98)80053-8 . Gori S, Soczewski E, Fernández L, Grasso E, Gallino L, Merech F, et al. Decidualization Process Induces Maternal Monocytes to Tolerogenic IL-10-Producing Dendritic Cells (DC-10). Front Immunol. 2020;11:1571. 10.3389/fimmu.2020.01571 . Gemelli C, Martello A, Montanari M, Zanocco Marani T, Salsi V, Zappavigna V, et al. The Orosomucoid 1 protein is involved in the vitamin D - mediated macrophage de-activation process. Exp Cell Res. 2013;319(20):3201–13. 10.1016/j.yexcr.2013.08.017 . Spiller F, Carlos D, Souto FO, de Freitas A, Soares FS, Vieira SM, et al. α1-Acid glycoprotein decreases neutrophil migration and increases susceptibility to sepsis in diabetic mice. Diabetes. 2012;61(6):1584–91. 10.2337/db11-0825 . Liu M, Wang J, Han Y, Fu X, Pan Y, Yang C, et al. Comprehensive landscapes of the causal network between immunity and sarcopenia. Front Immunol. 2024;15:1443885. 10.3389/fimmu.2024.1443885 . Chen Z, Sun J, Shi T, Song C, Wu C, Wu Z, et al. Causal roles of circulating cytokines in sarcopenia-related traits: a Mendelian randomization study. Front Endocrinol. 2024;15:1370985. 10.3389/fendo.2024.1370985 . Li CW, Yu K, Shyh-Chang N, Jiang Z, Liu T, Ma S, et al. Pathogenesis of sarcopenia and the relationship with fat mass: descriptive review. J cachexia sarcopenia muscle. 2022;13(2):781–94. 10.1002/jcsm.12901 . Xue J, Han X, Zheng Y, Zhang Q, Kong L. Effectiveness of resistance training in modulating inflammatory biomarkers among Asian patients with sarcopenia: a systematic review and meta-analysis of randomized controlled trials. Front Immunol. 2024;15:1385902. 10.3389/fimmu.2024.1385902 . Park MJ, Choi KM. Interplay of skeletal muscle and adipose tissue: sarcopenic obesity. Metab Clin Exp. 2023;144:155577. 10.1016/j.metabol.2023.155577 . Fielding RA, Atkinson EJ, Aversa Z, White TA, Heeren AA, Achenbach SJ, et al. Associations between biomarkers of cellular senescence and physical function in humans: observations from the lifestyle interventions for elders (LIFE) study. GeroScience. 2022;44(6):2757–70. 10.1007/s11357-022-00685-2 . Sun Y, Qin Z, Wan JJ, Wang PY, Yang YL, Yu JG, et al. Estrogen weakens muscle endurance via estrogen receptor-p38 MAPK-mediated orosomucoid (ORM) suppression. Exp Mol Med. 2018;50(3):e463. 10.1038/emm.2017.307 . Tezze C, Romanello V, Desbats MA, Fadini GP, Albiero M, Favaro G, et al. Age-Associated Loss of OPA1 in Muscle Impacts Muscle Mass, Metabolic Homeostasis, Systemic Inflammation, and Epithelial Senescence. Cell Metabol. 2017;25(6):1374–e896. 10.1016/j.cmet.2017.04.021 . Granic A, Suetterlin K, Shavlakadze T, Grounds MD, Sayer AA. Hallmarks of ageing in human skeletal muscle and implications for understanding the pathophysiology of sarcopenia in women and men. Clinical science (London, England: 1979). 2023;137(22):1721-51. 10.1042/CS20230319 Kerr HL, Krumm K, Anderson B, Christiani A, Strait L, Li T, et al. Mouse sarcopenia model reveals sex- and age-specific differences in phenotypic and molecular characteristics. J Clin Investig. 2024;134(16). 10.1172/JCI172890 . Hetherington-Rauth M, Johnson E, Migliavacca E, Langsetmo L, Hepple RT, Ryan TE et al. The Mediating Role of Kynurenine Pathway Metabolites on the Relationship Between Inflammation and Muscle Mass in Oldest-Old Men. The journals of gerontology Series A, Biological sciences and medical sciences. 2024;79(7). 10.1093/gerona/glae131 Xu Y, Wang D, Chen P, Qi B, Li X, Xie C, et al. Factors associated with skeletal muscle mass in middle-aged men living with HIV. J cachexia sarcopenia muscle. 2024;15(5):1965–75. 10.1002/jcsm.13545 . Halpern AL, Boshier PR, White AM, Houk AK, Helmkamp L, Mitchell JD, et al. A Comparison of Frailty Measures at Listing to Predict Outcomes After Lung Transplantation. Ann Thorac Surg. 2020;109(1):233–40. 10.1016/j.athoracsur.2019.07.040 . Rondanelli M, Klersy C, Terracol G, Talluri J, Maugeri R, Guido D, et al. Whey protein, amino acids, and vitamin D supplementation with physical activity increases fat-free mass and strength, functionality, and quality of life and decreases inflammation in sarcopenic elderly. Am J Clin Nutr. 2016;103(3):830–40. 10.3945/ajcn.115.113357 . Liu C, Cheung WH, Li J, Chow SK, Yu J, Wong SH, et al. Understanding the gut microbiota and sarcopenia: a systematic review. J cachexia sarcopenia muscle. 2021;12(6):1393–407. 10.1002/jcsm.12784 . Kim YA, Keogh JB, Clifton PM. Probiotics, prebiotics, synbiotics and insulin sensitivity. Nutr Res Rev. 2018;31(1):35–51. 10.1017/S095442241700018X . Chen LH, Chang SS, Chang HY, Wu CH, Pan CH, Chang CC, et al. Probiotic supplementation attenuates age-related sarcopenia via the gut-muscle axis in SAMP8 mice. J cachexia sarcopenia muscle. 2022;13(1):515–31. 10.1002/jcsm.12849 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 24 Jun, 2025 Editor invited by journal 27 May, 2025 Editor assigned by journal 27 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 25 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6743872","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463604098,"identity":"7a5036bd-0966-49b1-b18d-7472deca617e","order_by":0,"name":"Shuo 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participant selection from NHANES, 2015-2018\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6743872/v1/baea55d2ff494bcc365dedef.png"},{"id":84773550,"identity":"ac84af32-4419-4c5e-ac78-19ac2aa03858","added_by":"auto","created_at":"2025-06-17 08:36:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":286265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis and interaction analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssociation between AGP and sarcopenia, each stratification was adjusted for Age; Race and ethnicity; Education level; PIR; Diabetes; Physical activities; Hypoalbuminemia; Albumin(g/L); CRP(mg/L); Direct HDL-Cholesterol(mmol/L); Total Cholesterol(mmol/L); Vitamin D (nmol/L); Glycohemoglobin (%); Squares indicate odds ratios (ORs), with horizontal lines indicating 95% Cl.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6743872/v1/d9291d00e13b569900a55134.png"},{"id":84773266,"identity":"594c21f6-4079-406e-98bd-b6009e965312","added_by":"auto","created_at":"2025-06-17 08:28:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted spline curve for the association between the levels of serum AGP and sarcopenia using restricted cubic spline regression model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6743872/v1/c171af7333bfb8e055205171.png"},{"id":84773554,"identity":"3048d38c-50a9-4976-99f9-7f4c9c03b526","added_by":"auto","created_at":"2025-06-17 08:36:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139325,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensitivity analysis for the restricted cubic spline by adjusting the models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea: analysis of restricted cubic spline sarcopenia using the fully adjusted model (Model 3), as shown in Figure 1 in the main text; b: RCS using the unadjusted model (Model 1); c: RCS using the minimally adjusted (Model 2).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6743872/v1/399ae8e5cef7cec0e22c8bbe.png"},{"id":84776259,"identity":"0c0608d7-45c8-4aff-ad0e-5c4419a2352e","added_by":"auto","created_at":"2025-06-17 09:00:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1911810,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6743872/v1/8e59fdde-b045-421d-a838-f255828cfafe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum α-1 acid glycoprotein as a potential biomarker is associated with sarcopenia among American women aged 20-49: A cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia, a progressive skeletal muscle disorder characterized by loss of muscle mass, strength, and function, has emerged as a critical public health challenge [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While traditionally viewed as a geriatric condition, accumulating evidence highlights its early onset in middle-aged populations, with women exhibiting twice the prevalence compared to men [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This gender disparity underscores the urgency to identify early biomarkers and modifiable risk factors, particularly in younger cohorts, to mitigate downstream consequences such as physical disability, metabolic dysfunction, and increased mortality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Current diagnostic reliance on imaging modalities such as DXA and muscle strength measurements limits early detection. This emphasizes the need for accessible biomarkers to stratify risk and guide interventions, which are simpler and more cost-effective [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pathophysiology of sarcopenia remains multifactorial, involving intertwined mechanisms of chronic inflammation, oxidative stress, and metabolic dysregulation. Among these, low-grade systemic inflammation has gained prominence as a key driver of muscle catabolism [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Pro-inflammatory cytokines, including interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), contribute to impaired muscle protein synthesis and increased insulin resistance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the role of acute-phase proteins, which bridge inflammation and metabolic pathways, remains underexplored [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Given the intricate pathophysiological mechanisms underlying sarcopenia, it is probable that specific panels of biomarkers are required to effectively capture the onset and progression of sarcopenia through multiple pathways [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Considering the inflammation-mediated pathway, we focused on α-1 acid glycoprotein.\u003c/p\u003e \u003cp\u003eα-1 acid glycoprotein (AGP), produced by hepatocytes, is a sensitive indicator of systemic inflammation and immune activation. AGP can hinder insulin-driven glucose oxidation in skeletal muscle. It also has anti-fatigue properties, activates AMPK by binding to the CCR5 receptor, and boosts muscle glycogen synthesis and endurance [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Despite these mechanistic insights, epidemiological data linking AGP to sarcopenia are scarce, particularly in non-elderly populations.\u003c/p\u003e \u003cp\u003eNotably, existing studies on sarcopenia biomarkers, such as CRP and IL-6, have yielded inconsistent associations, highlighting the complexity of inflammatory pathways in muscle health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. AGP, with its unique dual role in regulating inflammation and energy metabolism, emerges as a compelling biomarker candidate for sarcopenia in young and middle-aged women. Previous classic studies have indicated that serum AGP concentration levels tend to increase with age in the general population, with a more pronounced effect observed in women [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While prior studies have established its diagnostic utility in distinguishing metabolically healthy women from those with metabolic syndrome (MetS), particularly in overweight/obese populations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], this investigation pioneers the exploration of AGP's relationship with sarcopenia in women. Addressing this gap is critical, as early identification of at-risk individuals could enable targeted anti-inflammatory or nutritional strategies to delay disease progression [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis population-based cross-sectional analysis employed data from the nationally representative NHANES dataset to investigate the association between circulating AGP concentrations and sarcopenia risk among women aged 20\u0026ndash;49. By focusing on a younger demographic, this work challenges the conventional geriatric framework of sarcopenia and provides novel insights into AGP\u0026rsquo;s potential as a biomarker for early muscle decline.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eData from the National Health and Nutrition Examination Survey (NHANES) was utilized for this study. This survey provides an overview of the health and nutritional status of U.S. adults and children. A complex, stratified multistage design was implemented to ensure the cohort's representativeness. NHANES data includes demographic, medical history, and some laboratory tests and physiological measurement, which are publicly available. The NCHS Research Ethics Review Committee approved the NHANES research protocol. Detailed methodological specifications for NHANES, including sampling protocols, biomarker assays, and data collection procedures, are publicly accessible through the CDC's official repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), ensuring transparency and reproducibility of this secondary analysis.\u003c/p\u003e \u003cp\u003eWe used data from two NHANES two-year cycles (2015\u0026ndash;2016, and 2017\u0026ndash;2018) involving 19,226 participants for analysis in this study. Initially, we excluded all male participants, along with female participants whose ages fell outside the 20 to 49 age range. Next, participants lacking data on DXA and BMI were excluded, which determine the definition of sarcopenia. Then we excluded those missing AGP data. In addition, those with other incomplete data were excluded. The final analytical cohort comprised 1,417 eligible participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of AGP\u003c/h3\u003e\n\u003cp\u003eNHANES employed the Tina-quant Roche AAGP2 assay to assess α-1-acid glycoprotein (AGP). The principle of immunological agglutination is the basis of this assay. This method forms an antigen-antibody complex through the interaction of anti-AGP antibodies with sample antigens, leading to agglutination. The extent of agglutination is assessed using turbidimetry (AAGP2 Tina-quant α1-Acid Glycoprotein Gen.2 [package insert]). The laboratory and method were certified under the 1988 Clinical Laboratory Improvement Amendment. Each run involved duplicate analyses of either in-house serum QC pools at three levels or Roche QC pools at two levels, with validity assessed through a multi-rule QC program.\u003c/p\u003e\n\u003ch3\u003eDefinition of sarcopenia\u003c/h3\u003e\n\u003cp\u003eThe European Working Group on Sarcopenia in Older People 2 (EWGSOP2) guidelines indicate that, while contemporary understanding suggests that low muscle strength supersedes low muscle mass as the primary determinant of sarcopenia, the diagnosis of sarcopenia is ultimately established through the identification of diminished muscle quantity or quality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Appendicular skeletal muscle mass (ASM; summing upper and lower extremity muscle mass) was quantified via dual-energy x-ray absorptiometry. Sarcopenia diagnosis utilized the ASM/BMI ratio, with sex-specific thresholds (women\u0026thinsp;\u0026lt;\u0026thinsp;0.512; men\u0026thinsp;\u0026lt;\u0026thinsp;0.789) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eOur study included covariates to assess the potential impact on the AGP-sarcopenia relationship. Demographic data were gathered, including age (categorized into 20\u0026ndash;29, 30\u0026ndash;39, and 40\u0026ndash;49), race and ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), education level (less than 9th grade, 9-11th grade, high school graduate, some college or AA degree, and college graduate or above), and family poverty-income ratio (PIR) categories (1.3, 1.3\u0026ndash;1.8, 1.8).\u003c/p\u003e \u003cp\u003eThe biochemical parameters assessed were total cholesterol (mmol/L), direct HDL-cholesterol (mmol/L), 25-hydroxyvitamin D2\u0026thinsp;+\u0026thinsp;D3 (nmol/L), glycohemoglobin (%), serum albumin (g/L), and CRP (mg/L) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Hypoalbuminemia is characterized by a serum albumin level below 35 g/L. Furthermore, the Physical Activity Guidelines for Americans suggest over 75 minutes of vigorous or 150 minutes of moderate exercise weekly [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. We classified participants' activity levels using questionnaire data into active (meeting or exceeding guidelines), less active (below guidelines), and inactive (no activity). Body mass index (BMI) was categorized as under/normal weight (\u0026lt;\u0026thinsp;24.0 kg/m2), overweight (24.0\u0026ndash;28.0 kg/m2), and obesity (\u0026gt;\u0026thinsp;28.0 kg/m2). Diabetes diagnosis was based on glycohemoglobin levels\u0026thinsp;\u0026ge;\u0026thinsp;6.5% and patient self-reports.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eParticipants were stratified by sarcopenia status for analytical comparisons. Continuous measures were expressed as weighted means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, with group differences assessed through nonparametric comparative analysis (Kruskal-Wallis test). Categorical variables were reported as weighted proportions (95% CI) and analyzed using weighted Rao-Scott χ\u0026sup2; tests with Fisher's exact verification.\u003c/p\u003e \u003cp\u003eMulticollinearity was systematically evaluated through variance inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;10 criterion). Multivariable logistic regression with sequential adjustment strategy elucidated AGP-sarcopenia associations: Model 1 was unadjusted. Model 2 was adjusted for demographic data (age, race and ethnicity, education level, PIR). Model 3 was fully adjusted for behavioral, metabolic, and biochemical covariates (physical activity, diabetes, hypoalbuminemia, serum biomarkers).\u003c/p\u003e \u003cp\u003eExposure was parameterized as ordinal tertile variables (reference: first tertile) for trend analysis. Nonlinear dynamics were interrogated through weighted restricted cubic spline (RCS) regression with threshold detection algorithms.\u003c/p\u003e \u003cp\u003eAdditionally, subgroup analyses were conducted by examining age groups (20\u0026ndash;29/30\u0026ndash;39/40\u0026ndash;49), physical activity (inactive/ less active/ active), CRP groups (\u0026lt;1.05/ 1.05-4.00/ \u0026gt;4.00 mg/L), diabetes (yes/no), and hypoalbuminemia (yes/no). We incorporated interaction tests to evaluate the associations between various subgroups and exposure factors. Interaction testing was performed by incorporating multiplicative terms (e.g., AGP \u0026times; age, AGP \u0026times; CRP) into fully adjusted logistic regression models. P-values for interaction were derived using Wald tests.\u003c/p\u003e \u003cp\u003eNHANES employs a complex, multistage probability sampling design. To account for oversampling, nonresponse, and population representation, survey weights (WTMEC2YR) were multiplied by 2/4, as recommended by NHANES guidelines for combining multiple cycles. Analyses were conducted using Empower Stats (v2.0; X\u0026amp;Y Solutions Inc), and STATA (v17.0). A p-value below 0.05 was deemed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eThe study involved 1,417 female participants, averaging 34.4 years of age with a standard deviation of 8.7 years. Among these women, 6.8% were identified as having sarcopenia. Ulteriorly, the incidence in participants aged 20\u0026ndash;29 was 4.8%, while 5.8% in 30\u0026ndash;39, and 10% in 40\u0026ndash;49. Women with sarcopenia were more likely to be Mexican-American or other Hispanic and to have lower education (below high school) and income levels (PIR\u0026thinsp;\u0026le;\u0026thinsp;1.8) compared to those without the condition. Women with sarcopenia tended to be obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;28 kg/m\u0026sup2;, 37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3), exhibit hypoalbuminemia, and engage in low levels of physical activity. Furthermore, this cohort exhibited a marked increase in serum AGP and CRP levels, coupled with a relative decrease in HDL-C and 25-hydroxyvitamin D2\u0026thinsp;+\u0026thinsp;D3 concentrations, compared to individuals without sarcopenia. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a comprehensive overview of the baseline characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of participants in the NHANES 2015\u0026ndash;2018 cycles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1320(93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.7(31.6, 37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.1(31.9, 38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.3(17.8, 39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.7(29.7, 35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.0(29.8, 36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.4(18.9, 40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.6(29.4, 36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.9(28.6, 35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.3(32.7, 56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace and ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2(9.8, 12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.8(8.5, 11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.3(26.4, 47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3(6.1, 8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2(6.0, 8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.4(5.2, 16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.8(58.8, 64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.7(59.7, 65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.1(32.1, 56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0(7.8, 10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.4(8.1, 10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8(0.6, 5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7(9.2, 12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.9(9.2, 12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.4(3.6, 18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0(2.3, 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8(2.1, 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6(3.6, 11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0(4.8, 7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.3(4.2, 6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.3(11.8, 29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.3(16.0, 20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.1(15.7, 20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.9(12.8, 32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.4(33.3, 39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.4(33.2, 39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.0(25.2, 48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.4(33.0, 39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.4(33.9, 41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.2(9.1, 30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.6(22.2, 27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.9(21.4, 26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.0(27.6, 49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u0026ndash;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0(8.4, 11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.5(7.9, 11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.2(12.4, 31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.3(62.3, 68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.7(63.6, 69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.8(30.1, 54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.0(27.8, 34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.4(29.1, 35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2(1.9, 13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1(19.3, 25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.8(19.9, 25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0(4.1, 18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.0(43.6, 50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.8(41.4, 48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.8(75.5, 92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.2(95, 97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.2(94.9, 97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.0(91, 98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8(2.8, 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.8(2.8, 5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(1.7, 9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.1(18.6, 23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.1(17.6, 22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.6(27.9, 50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9(8.9, 13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.0(8.9, 13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3(4.4, 15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(64.8, 71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.8(65.5, 71.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.1(41.2, 64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypoproteinemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.6(95.2, 97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.1(95.7, 98.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.9(76, 94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4(2.4, 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9(1.9, 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.1(5.7, 24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-Cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.3\u0026thinsp;\u0026plusmn;\u0026thinsp;26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycohemoglobin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Reactive Protein (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGP (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.9\u0026thinsp;\u0026plusmn;\u0026thinsp;24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.0\u0026thinsp;\u0026plusmn;\u0026thinsp;23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.9\u0026thinsp;\u0026plusmn;\u0026thinsp;28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNHANES, National Health and Nutrition Examination Survey; SD, standard deviation; PIR, poverty income ratio; BMI, body mass index (calculated as kilograms divided by meters squared); Vitamin D: 25-hydroxyvitamin D2 and D3; Hypoproteinemia was defined as Albumin(g/L)\u0026thinsp;\u0026lt;\u0026thinsp;35g/L; Diabetes was defined as an HbA1c level\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or an FPG level\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, or a self-reported diagnosis of diabetes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe association between AGP and sarcopenia\u003c/h3\u003e\n\u003cp\u003ePrior to conducting the analyses, we assessed collinearity among the covariates. The VIF for all covariates was found to be less than 10, suggesting the absence of multicollinearity.\u003c/p\u003e \u003cp\u003eFirstly, AGP was conventionally treated as a continuous variable. The study identified a significant positive correlation between AGP levels and sarcopenia incidence, irrespective of confounder adjustments. A one-unit increase in AGP is associated with a 3% higher risk of developing sarcopenia in both model 1 (OR 1.03, 95% CI 1.02\u0026ndash;1.03, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and model 2 (OR 1.03, 95% CI 1.02\u0026ndash;1.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a 2% increase in model 3 (OR 1.02, 95% CI 1.01\u0026ndash;1.03, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A one-SD increase in AGP corresponds to higher risk of sarcopenia. Meanwhile, when AGP was converted into a categorical variable by tertile, the study showed analogously positive results. Individuals in the highest AGP tertile exhibited a significantly increased risk of sarcopenia compared to those in the lowest tertile across all models: model 1 (OR 4.87, 95% CI 2.41\u0026ndash;9.86; P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), model 2 (OR 4.72, 95% CI 2.31\u0026ndash;9.66; P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and model 3 (OR 3.42, 95% CI 1.50\u0026ndash;7.77; P for trend\u0026thinsp;=\u0026thinsp;0.003). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of the AGP with the risk of Sarcopenia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGP (mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 OR, (95% CI), \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2 OR, (95% CI), \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3 OR, (95% CI), \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer 1 unit increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03(1.02, 1.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03(1.02,1.04)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02(1.01, 1.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer 1 SD increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.80(1.45, 2.24)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.89(1.48, 2.42)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44(1.08, 1.93) 0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGP Tertile 1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGP Tertile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.03(1.45, 6.31) 0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.68(1.24, 5.79) 0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.21(0.95, 5.13) 0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGP Tertile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.87(2.41, 9.86)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.72(2.31, 9.66)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.42(1.50, 7.77) 0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOR, Odds Ratio; Cl, Confidence Interval; ref, Reference; Model 1: Non-adjusted model; Model 2 adjusted for: Age; Race and ethnicity; Education level; PIR; Model 3 adjusted for: Age; Race and ethnicity; Education level; PIR; Diabetes; Physical activities; Albumin(g/L); CRP(mg/L); Direct HDL-Cholesterol(mmol/L); Total Cholesterol(mmol/L); Vitamin D(nmol/L); Glycohemoglobin (%).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis and interaction test\u003c/h2\u003e \u003cp\u003eSubsequent analysis utilized the fully adjusted model (model 3). For more comprehensive results, CRP was grouped by tertile. Subgroup analyses were performed considering age, physical activity, CRP levels, Vitamin D levels, hypoalbuminemia status, and diabetes status. Serum AGP levels showed significant positive correlations with sarcopenia in participants aged 30\u0026ndash;39 years (P\u0026thinsp;=\u0026thinsp;0.010), those with hypoalbuminemia (P\u0026thinsp;=\u0026thinsp;0.041), non-diabetic individuals (P\u0026thinsp;=\u0026thinsp;0.016), and those with CRP levels below 1.05 mg/L (P\u0026thinsp;=\u0026thinsp;0.004). Beyond that, interaction tests showed obvious joint effect between CRP and AGP (Interaction P\u0026thinsp;=\u0026thinsp;0.018). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAssociation between AGP and sarcopenia, each stratification was adjusted for Age; Race and ethnicity; Education level; PIR; Diabetes; Physical activities; Hypoalbuminemia; Albumin(g/L); CRP(mg/L); Direct HDL-Cholesterol(mmol/L); Total Cholesterol(mmol/L); Vitamin D (nmol/L); Glycohemoglobin (%); Squares indicate odds ratios (ORs), with horizontal lines indicating 95% Cl.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNonlinear association between AGP and sarcopenia\u003c/h2\u003e \u003cp\u003eOtherwise, as indicated by RCS, the relationship between the AGP level and sarcopenia was nonlinear (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We performed a sensitivity analysis by transforming the adjusted models, which verified the robustness of the findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This complex association was parametrically analyzed through piecewise regression modeling. The AGP level's inflection point was identified at 76.3. A positive correlation was identified when serum AGP concentration was under 76.3 (OR 1.09, 95% CI 1.03\u0026ndash;1.15, P\u0026thinsp;=\u0026thinsp;0.003). A slightly weaker positive correlation was observed when the concentration surpassed 76.3 (OR 1.02, 95% CI 1.00\u0026ndash;1.04, P\u0026thinsp;=\u0026thinsp;0.048). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ea: analysis of restricted cubic spline sarcopenia using the fully adjusted model (Model 3), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the main text; b: RCS using the unadjusted model (Model 1); c: RCS using the minimally adjusted (Model 2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreakpoint. OR (95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point (K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR1, (95% CI) (\u0026lt;\u0026thinsp;K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09(1.03, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR1, (95% CI) (\u0026gt;\u0026thinsp;K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02(1.00, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikelihood Ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUtilizing NHANES 2015\u0026ndash;2018 data, this cross-sectional study offers new insights into the relationship between serum α-1 acid glycoprotein (AGP) and sarcopenia in American women aged 20\u0026ndash;49 years. The study determined that AGP levels are associated with the risk of sarcopenia in young women, exhibiting a nonlinear threshold effect at a cutoff point of 76.3 mg/dL. These results not only align with emerging evidence on the role of chronic inflammation in muscle degeneration but also extend current knowledge by highlighting AGP\u0026rsquo;s unique position linking inflammatory and metabolic pathways in younger female populations. Below, we contextualize these findings within existing literature, explore potential mechanisms, and address implications for research and clinical practice.\u003c/p\u003e \u003cp\u003ePrevious research on neonatal swine has demonstrated a correlation between elevated levels of AGP and both impaired growth and diminished muscle mass [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our study's findings align with these observations. The observed association between AGP and sarcopenia corroborates preclinical studies implicating chronic low-grade inflammation in muscle catabolism. AGP, an acute-phase reactant, is produced in response to pro-inflammatory cytokines like IL-6 and TNF-α, which impair muscle protein synthesis and induce insulin resistance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our findings suggest that AGP may exacerbate sarcopenia through dual mechanisms: 1) directly diminishing insulin-stimulated glucose oxidation, and specifically reducing protein synthesis, as demonstrated in experimental models of mouse C2C12 myotubes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and 2) amplifying systemic inflammation, thereby accelerating muscle atrophy. It has been shown that, increased AGP and decreased albumin during inflammation can cause muscle atrophy by boosting proteolysis and hindering synthesis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the nonlinear relationship\u0026mdash;characterized by a stronger association below the inflection point (76.3 mg/dL)\u0026mdash;hints at a saturation effect or a shift in AGP\u0026rsquo;s biological role at higher concentrations. At lower levels (\u0026lt;\u0026thinsp;76.3 mg/dL), AGP may serve as a sensitive predictor of sarcopenia risk. Beyond this threshold, its predictive utility diminishes, potentially due to compensatory mechanisms (e.g., anti-inflammatory pathways) or confounding factors like chronic inflammation. For instance, elevated AGP levels might transition from pro-inflammatory to immunoregulatory functions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], attenuating its direct impact on muscle loss. This hypothesis aligns with AGP\u0026rsquo;s known ability to modulate immune cell activity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and suppress neutrophil adhesion at higher thresholds [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], a mechanism that warrants further investigation.\u003c/p\u003e \u003cp\u003eThe interaction between AGP and CRP further underscores the complexity of inflammatory pathways in sarcopenia. While both biomarkers are acute-phase reactants, their divergent associations with muscle health highlight the multifaceted nature of \"inflammaging\"\u0026mdash;the age-related chronic inflammation implicated in sarcopenia [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Intriguingly, our subgroup analysis revealed that AGP\u0026rsquo;s association with sarcopenia persisted even in individuals with low CRP levels (\u0026lt;\u0026thinsp;1.05 mg/L). This suggests that AGP may capture distinct inflammatory pathways, such as cytokine-driven hepatic synthesis or tissue-specific immune activation, which are not fully reflected by CRP. Furthermore, the synergistic interaction between AGP and CRP (P\u0026thinsp;=\u0026thinsp;0.018) supports the hypothesis that cumulative inflammatory insults, rather than isolated markers, drive muscle decline [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings advocate for a multi-marker approach to stratify sarcopenia risk, particularly in populations where traditional biomarkers like CRP exhibit limited sensitivity.\u003c/p\u003e \u003cp\u003eThe stronger association between AGP and sarcopenia in women aged 30\u0026ndash;39 years challenges the conventional view of sarcopenia as a geriatric condition. This age-specific trend may reflect early inflammatory dysregulation in midlife, potentially triggered by hormonal changes during perimenopause, sedentary lifestyles, or subclinical metabolic disturbances [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Nonetheless, AGP is also identified as an endogenous anti-fatigue protein. An experimental study has indicated that women exhibit lower muscular endurance compared to men, potentially due to estrogen's down-regulation of AGP levels [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In conjunction with our findings, this suggests that AGP may possess a complex regulatory role in skeletal muscle metabolism.\u003c/p\u003e \u003cp\u003eMitochondrial dysfunction, a hallmark of aging, may also play a role: impaired energy metabolism in muscle tissue could exacerbate oxidative stress, creating a pro-inflammatory milieu that upregulates AGP synthesis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In a study aimed at elucidating sex differences in the mechanisms underlying mouse sarcopenia, it was observed that males exhibited diminished mitochondrial biogenesis, oxidative capacity, and AMPK-autophagy signaling during the progression of sarcopenia. In contrast, these attributes were not detected in females [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Importantly, our results emphasize the need for early monitoring of inflammatory markers in younger at-risk populations, as interventions during this critical window\u0026mdash;such as anti-inflammatory therapies or lifestyle modifications\u0026mdash;may delay or mitigate muscle loss before irreversible damage occurs. Elsewhere, a cross-sectional study focusing on oldest-old men have suggested that AGP indirectly affects D3-creatine dilution muscle mass through the kynurenine pathway metabolites [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This also provides a theoretical basis for extending the research on AGP and sarcopenia to the whole population in the future.\u003c/p\u003e \u003cp\u003eThe heightened sarcopenia risk observed in women with hypoalbuminemia (serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;35 g/L) underscores the interplay between nutritional status and inflammation. Albumin, a negative acute-phase protein, declines during systemic inflammation, while AGP, a positive acute-phase reactant, rises. This inverse relationship suggests that hypoalbuminemia may amplify AGP\u0026rsquo;s detrimental effects on muscle protein metabolism. For instance, low albumin levels could reduce oncotic pressure, exacerbating muscle edema [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and impairing nutrient delivery to skeletal muscle [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Conversely, AGP\u0026rsquo;s binding affinity for drugs and hormones might disrupt anabolic signaling pathways, further compromising muscle maintenance. The study underscores the need for comprehensive approaches that tackle both inflammation and malnutrition in preventing sarcopenia, such as the concurrent use of whey protein supplements to enhance albumin production and anti-inflammatory agents aimed at AGP [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent advances in the gut-muscle axis provide additional context for our findings. Gut microbiota dysbiosis is associated with systemic inflammation and muscle atrophy through mechanisms involving bacterial lipopolysaccharides (LPS), short-chain fatty acids (SCFAs), and immune cell activation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. AGP, as a modulator of intestinal barrier function, may indirectly influence muscle health by regulating gut permeability and microbial translocation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Preclinical studies suggest that probiotics and prebiotics can attenuate muscle loss by reducing circulating LPS and pro-inflammatory cytokines, thereby suppressing AGP synthesis [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Future research should explore whether AGP serves as a mediator in the gut-muscle axis, bridging microbial metabolites to inflammatory pathways in sarcopenia.\u003c/p\u003e \u003cp\u003eBased on the above discussion, this study has the value of clinical transformation. Clinically, AGP assays are already available in routine blood panels (immunology profiles) at costs comparable to CRP in many healthcare systems. However, standardization of AGP thresholds for sarcopenia remains lacking, and its adoption may require validation in diverse populations. Our findings highlight the clinical relevance of \"within-normal-range variations\", consistent with emerging concepts of \"subclinical biomarker\". If validated longitudinally, AGP measurement could complement existing tools for early sarcopenia detection, particularly in high-risk subgroups such as women with hypoalbuminemia, low CRP levels, or aged 30\u0026ndash;39 years. By enabling risk stratification during the subclinical phase, AGP may facilitate timely interventions\u0026mdash;such as resistance training, dietary modifications, or anti-inflammatory therapies\u0026mdash;to mitigate muscle loss before irreversible decline. Future research should prioritize AGP\u0026rsquo;s integration into multi-marker panels and explore its utility in diverse populations and clinical settings.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eWhile this study offers valuable insights, several limitations must be acknowledged. As a cross-sectional study, causality cannot be inferred. and the focus on young/middle-aged women limits generalizability to older adults or males. Longitudinal studies are needed to validate AGP\u0026rsquo;s predictive value and elucidate its temporal relationship with muscle decline. Mechanistic research should investigate AGP\u0026rsquo;s direct effects on myocyte apoptosis, satellite cell activation, and mitochondrial function. Clinically, interventions targeting AGP-related pathways\u0026mdash;such as IL-6 inhibitors, nutritional strategies to correct hypoalbuminemia, or gut microbiota modulation\u0026mdash;warrant exploration in randomized trials.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study establishes AGP as a promising biomarker for sarcopenia, particularly in younger women, and underscores the interplay between inflammation, metabolism, and nutrition in muscle health. By identifying a nonlinear threshold and age-specific associations, our findings advocate for personalized approaches to sarcopenia risk stratification and intervention. Future research should build on these results to unravel AGP\u0026rsquo;s mechanistic role and translate these insights into targeted therapies for at-risk populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSZ: Visualization, Conceptualization, Data curation, Formal Analysis, Software and Writing - original draft. JC: Conceptualization, Data curation, Formal Analysis, Methodology and Writing - original draft, review \u0026amp; editing. SZ and JC contributed equally to this work and share first authorship. CD: conceptualization. WL: Methodology. HZ: Data curation. DW: Data curation. YZ: Software. SZ (Sihao Zhao): Visualization. HW: Conceptualization, Supervision, Validation and Writing - review \u0026amp; editing. QL: Conceptualization, Supervision, Validation, Project administration, Funding acquisition, and Writing - review \u0026amp; editing. All authors participated in the development of this article and have given their approval for the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Capital Clinical Characteristic Diagnosis and Treatment Technology Research and Translational Application Project (Z221100007422122).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES datasets for this study can be found in the CDC\u0026apos;s official repository (https://www.cdc.gov/nchs/nhanes/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the participants and staff of NHANES for their valuable work and dedication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Consent to participate was obtained and the National Center for Health Statistics (NCHS) ethics committee approved NHANES study protocol. Study protocols for NHANES were approved by the NCHS ethnics review board (Protocol #2011-17). The participants provided their written informed consent before taking part in this study. All information from the NHANES program is available and free for the public, so the agreement of the medical ethics committee board was not necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that the research was conducted without any commercial or financial affiliations that could be interpreted as potential conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSayer AA, Cruz-Jentoft A. Sarcopenia definition, diagnosis and treatment: consensus is growing. 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J cachexia sarcopenia muscle. 2022;13(1):515\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcsm.12849\u003c/span\u003e\u003cspan address=\"10.1002/jcsm.12849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"α-1 acid glycoprotein, sarcopenia, inflammation, NHANES, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-6743872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6743872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe role of α-1 acid glycoprotein (AGP) as an inflammatory marker in sarcopenia remains unclear. This study, concentrating on young women, investigated the potential of serum AGP as a biomarker for stratifying the risk of sarcopenia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing nationally representative data from 1,417 women (20\u0026ndash;49 years) in the National Health and Nutrition Examination Survey, we conducted a multivariable-adjusted analysis through weighted logistic regression models. Stratified analyses incorporated interaction testing across clinically relevant subgroups. Nonlinear associations were interrogated using restricted cubic spline modeling with threshold detection algorithms, and sensitivity analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the fully adjusted model, each 1-unit rise in AGP concentration increased sarcopenia risk by 2% (OR 1.02, 95% CI 1.01\u0026ndash;1.03). This link was more evident in individuals aged 30\u0026ndash;39, without diabetes, with hypoalbuminemia, and CRP levels below 1.05 mg/L. Additionally, RCS and threshold analysis demonstrated a nonlinear relationship, identifying a turning point at 76.3 mg/dL.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings corroborate the association between AGP levels and the risk of sarcopenia in women aged 20 to 49 years. This positions AGP quantification as a promising biomarker for early detection and risk stratification of sarcopenia.\u003c/p\u003e","manuscriptTitle":"Serum α-1 acid glycoprotein as a potential biomarker is associated with sarcopenia among American women aged 20-49: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 08:28:00","doi":"10.21203/rs.3.rs-6743872/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-06-24T12:02:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-27T11:16:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-27T09:14:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T09:09:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-05-25T13:04:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"72bcf593-35b0-4b0c-a304-1eccc56f4f00","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-24T12:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 08:28:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6743872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6743872","identity":"rs-6743872","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}