Serum Cortisol as an Independent Biomarker for Disease Severity and a Modifiable Factor in Nausea and Vomiting of Pregnancy: A Secondary Analysis of a Randomized Controlled Trial

Serum Cortisol as an Independent Biomarker for Disease Severity and a Modifiable Factor in Nausea and Vomiting of Pregnancy: A Secondary Analysis of a Randomized Controlled Trial | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 January 2026 V1 Latest version Share on Serum Cortisol as an Independent Biomarker for Disease Severity and a Modifiable Factor in Nausea and Vomiting of Pregnancy: A Secondary Analysis of a Randomized Controlled Trial Authors : Baichao Shi 0000-0001-9559-4611 , Hong Yu , Zhihui Zhang , Fengjuan Lu , Muxin Guan , Jiannan Yu , Zhuwei Gao , Yue Gao , Jiaxing Feng , Jing Cong , and Yu Wang Authors Info & Affiliations https://doi.org/10.22541/au.176971690.09278552/v1 149 views 63 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: The pathophysiology of nausea and vomiting of pregnancy (NVP) remains incompletely elucidated. This study investigated whether serum cortisol level is associated with NVP severity, exploring its potential role as a biomarker. Methods: This secondary analysis used data from a randomized controlled trial involving 352 first-trimester women with moderate-to-severe NVP. Participants were stratified based on cortisol quartiles. Comprehensive data, including anthropometric, biochemical, and quality of life measures, were collected. Linear and logistic regression analyses assessed the associations between cortisol and clinical parameters and the risk of severe NVP. Receiver operating characteristic (ROC) analysis evaluated cortisol’s predictive performance. Changes in cortisol levels following treatment were also conducted. Results: Higher cortisol levels were independently associated with increased NVP severity, showing positive correlations with PUQE and VAS scores, alongside a significant association with impaired quality of life. Cortisol was positively correlated with liver enzymes, urea nitrogen, creatinine, free thyroid hormones, calcium, and magnesium, but inversely associated with chloride, potassium, and vitamin B1. Participants in the highest cortisol quartile had a significantly increased risk of severe NVP compared to the lowest quartile. ROC analysis indicated modest predictive value for severe NVP. Serum cortisol levels decreased significantly after treatment across all intervention groups. Subgroup analysis revealed that the positive association between cortisol and severe NVP was more significant in placebo subgroups than in doxylamine-pyridoxine subgroups. Conclusion: Elevated serum cortisol is independently associated with greater NVP severity and reduced quality of life. The observed decrease following effective intervention verifies cortisol as a promising biomarker reflective of disease severity and a potentially modifiable factor in the pathophysiology of NVP. Keywords: Nausea and vomiting of pregnancy; Cortisol; Severity; Biomarker; Stress Hormone 1. Introduction Nausea and vomiting of pregnancy (NVP) is one of the most common complaints in early gestation, approximately 35% to 91% of pregnant women worldwide[1]. Symptom onset typically occurs between 6–8 weeks of gestation and usually resolves by 16–20 weeks[2]. In 1%–2% of cases, NVP progresses to hyperemesis gravidarum (HG)[3], a severe form characterized by marked electrolyte imbalances, acid-base disturbances, nutritional deficiencies, and weight loss[4]. This condition profoundly impairs sleep quality and overall quality of life, thereby disrupting daily functioning[5]. Left untreated, HG is associated with serious maternal and fetal complications, including Wernicke’s encephalopathy, acute kidney injury, threatened abortion, placental abruption, preterm birth, and infants who are small for gestational age or have low birth weight[6]. The pathophysiology of NVP or HG remains incompletely understood. It is considered to have a multifactorial etiology, involving a complex interplay of endocrine, metabolic, and neuroregulatory factors. Cortisol, a central mediator of the hypothalamic-pituitary-adrenal (HPA) axis, exhibits a pronounced 24-hour (or circadian) rhythmicity and plays a pivotal role in the body’s stress response and metabolic regulation[7]. Pregnancy induces a physiological escalation in HPA axis activity and cortisol levels, a state that can be potentiated by concurrent psychological or physiological stressors[8]. While emerging evidence links psychological distress to NVP severity, implicating cortisol as a potential pathogenic factor, the precise role of cortisol in the development and progression of NVP remains ambiguous[9, 10]. A critical knowledge gap exists in understanding the interrelationships between cortisol levels and NVP severity. Previous studies on NVP have primarily focused on hormonal factors, such as elevated human chorionic gonadotropin (hCG) and thyroid hormones, as well as nutritional factors, including vitamins and microelements[4, 11, 12]. More recently, growth differentiation factor 15 (GDF15) has been identified as a key regulator in its pathogenesis[13]. In comparison, the dynamic changes of cortisol and their correlations with multisystem physiological parameters remain inadequately explored in earlier literature. Additionally, while standard treatments such as the doxylamine-pyridoxine combination and acupuncture are clinically effective[14, 15], their potential to modulate HPA axis activity and cortisol levels remains a key question regarding their underlying mechanisms. Therefore, this study conducted a secondary analysis based on data from a multicenter randomized controlled trial (RCT) conducted in mainland China—the NVP Acupuncture and Doxylamine/Pyridoxine Trial (NVPAct). It aimed to systematically investigate: (1) the independent association between serum cortisol and the severity of clinical NVP symptoms as well as quality of life scores; (2) the relationship of cortisol with multisystem physiological parameters, including hepatic, renal, and thyroid function, electrolyte balance, vitamins, and neuroendocrine markers; (3) the potential of cortisol as a predictive biomarker for severe NVP; and (4) the effects of different interventions (pharmacological and acupuncture) on cortisol levels and their variation by treatment modality. By integrating systematic demographic, laboratory, and patient-reported outcome data, this study provides novel insights into the role of cortisol in NVP pathogenesis, thereby establishing a theoretical foundation for future personalized therapeutic strategies. 2. Methods 2.1 Design and target population This secondary analysis was based on data obtained from the NVPAct, which was carried out in mainland China between 2020 and 2022. The study recruited 352 individuals in their first trimester who were diagnosed with moderate-to-severe NVP, as assessed by the Pregnancy-Unique Quantification of Emesis and Nausea (PUQE) score—currently the most widely adopted symptom-based instrument for evaluating NVP severity[16]. Participants were evenly distributed into four study arms: (1) active acupuncture plus doxylamine–pyridoxine, (2) sham acupuncture plus doxylamine–pyridoxine, (3) active acupuncture plus placebo, and (4) sham acupuncture plus placebo. Comprehensive details regarding the trial design, inclusion/exclusion criteria, and primary outcome have been published elsewhere[17]. This clinical trial was registered on ClinicalTrials.gov (NCT04401384). The study received ethical approval from the Institutional Review Board of the First Affiliated Hospital of Heilongjiang University of Chinese Medicine (HZYLLKY201902301). Written informed consent was obtained from all participants prior to their enrollment in the study. 2.2 Data collection 2.2.1 Anthropometric Characteristics At the baseline enrollment visit, the following data were collected: age (year), spouse’s age (year), height (cm), weight (kg), body mass index (BMI, kg/m 2 ) and categorized as follows: underweight/normal weight: BMI < 24.0 kg/m²; overweight: BMI 24.0 to < 28.0 kg/m²; and obesity: BMI ≥ 28.0 kg/m²[18], systolic blood pressure (SBP, mmHg), diastolic blood pressure (DBP, mmHg), respirations (beat), pulse (beat), age at menarche (year), mean menstrual cycle (day), duration of NVP at recruitment (day), and smoking history of participants and their husband. 2.2.2 Laboratory Measurements Venous blood samples were collected during baseline visits. All samples will be analyzed by the Central Laboratory of Heilongjiang University of Chinese Medicine Laboratory. The following parameters were included: (1) Hepatic function: alanine aminotransferase (ALT, U/L), aspartate aminotransferase (AST, U/L), and alkaline phosphatase (ALP, U/L); (2) Renal function: urea nitrogen (UREA, mmol/L) and creatinine (CREA, μmol/L); (3) Thyroid function: thyroid-stimulating hormone (TSH, mIU/L), free triiodothyronine (FT3, pmol/L), and free thyroxine (FT4, pmol/L); (4) Electrolyte levels: potassium (K, mmol/L), sodium (Na, mmol/L), chloride (Cl, mmol/L), calcium (Ca, mmol/L), magnesium (Mg, mmol/L), phosphorus (P, mmol/L), iron (Fe, μmol/L), and zinc (Zn, μmol/L); and (5) Vitamins: vitamin B1 (Vit B1, μg/L), vitamin B6 [pyridoxal 5’-phosphate] (Vit B61, nmol/L), vitamin B6 [pyridoxal] (Vit B62, nmol/L) and vitamin B12 (Vit B12, pmol/L); and (6) Neuroendocrine biomarkers: growth differentiation factor 15 (GDF15, pg/ml), substance P (SP, pg/ml), arginine vasopressin (AVP, pg/ml), insulin-like growth factor-binding protein 7 (IGFBP7, ng/ml), leptin (LEP, ng/ml), serotonin (5-HT, ng/ml), and ghrelin (ng/ml). 2.2.3 Quality of Life Measures The study employed the following validated questionnaires for assessment: The PUQE score (range of 3 to 15) comprises three domains: duration of nausea, frequency of vomiting, and episodes of retching, with a score < 6 indicating mild NVP, 6–12 indicating moderate NVP, and ≥ 13 indicating severe NVP. The Visual Analogue Scale (VAS) score ranges from 0 to 10, where a higher score reflects a greater severity of nausea and vomiting. The Global Assessment of Well-being is scored from 0 to 10, with higher scores representing a better sense of well-being. The NVP-specific Quality of Life (NVPQoL) questionnaire (range from 30 to 210) encompasses four dimensions: physical symptoms, fatigue, emotions, and limitations, with higher scores indicating poorer quality of life. The Zung Self-Rating Anxiety Scale (SAS) scores range from 25 to 100, with elevated scores suggesting more severe anxiety. Similarly, the Zung Self-Rating Depression Scale (SDS) scores range from 25 to 100, where higher scores are indicative of more severe depression. 2.3 Statistical analyses Statistical analyses were conducted using IBM SPSS Statistics (version 26.0). Continuous variables are described as mean ± standard deviation (SD), while categorical variables are summarized as frequency (percentage). Group comparisons for normally distributed continuous variables were performed using analysis of variance (ANOVA). The Kruskal–Wallis H test was applied for continuous variables that deviated from normality. Differences in categorical variables among groups were examined using the chi-square (χ²) test. Differences in continuous variables between pre- and post-treatment groups were analyzed using Student’s t-test for normally distributed data and the Mann-Whitney U test for skewed distributions. Linear regression analysis was employed to evaluate correlations between the cortisol and anthropometric as well as biochemical parameters. Multivariable logistic regression models were used to estimate odds ratio (OR) and 95% confidence intervals (CI) for the associations of cortisol with NVP severity. The predictive performance of cortisol for NVP severity was assessed using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) used to quantify discriminative ability. Optimal cutoff values for cortisol were determined by maximizing the Youden index (sensitivity + specificity − 1). A P -value < 0.05 was considered statistically significant. 3. Results A total of 349 participants were included in the analysis, all of whom had cortisol measurements. They were classified into four quartiles according to cortisol values: quartile 1 (Q1) ≤ 8.61, n = 88; quartile 2 (Q2) 8.62–11.80, n = 90; quartile 3 (Q3) 11.81–15.50, n = 84; and quartile 4 (Q4) ≥ 15.51, n = 87 ( Figure 1 ). Figure 1. Flowchart of the study population 3.1 The Anthropometric, Biochemical Measurements, and Quality of Life Sores Across the Quartiles of Cortisol The baseline demographic, anthropometric, and clinical characteristics of the participants, stratified by cortisol quartiles, are summarized in Table 1 . Among these parameters, only DBP and pulse rate exhibited significant trends across the groups ( P for trend = 0.001 and < 0.001, respectively). In contrast, all other measured characteristics—including age, height, weight, BMI, smoking history, spouses’ characteristics, gestational age, and the duration of NVP at recruitment—documented no significant differences across the groups (all P > 0.05). An overarching biochemical profiling indicated significant alterations across cortisol quartiles. Markers of hepatic function (ALT, AST, ALP) demonstrated a concomitant rise with increasing cortisol levels ( P -trend for all < 0.05). Similarly, renal function markers, UREA and CREA, showed significant increasing trends ( P for trend = 0.001 and 0.012, respectively). Thyroid function was notably influenced, characterized by a significant elevation in FT3 and FT4 levels ( P for trend < 0.001 for both), in contrast to a non-significant decline in TSH ( P for trend = 0.130). Electrolyte analysis depicted an inverse association between Cl levels and cortisol ( P for trend < 0.001), in contrast to the positive associations observed for Ca and Mg ( P for trend < 0.001 and = 0.024, respectively). Among trace elements, Zn levels increased significantly with higher cortisol ( P for trend = 0.010). No discernible trends were identified for K, Na, P, or Fe (all P -trend > 0.05). Analysis of vitamins and neuroendocrine biomarkers illustrated strong associations with cortisol levels. A marked decrease was discovered for Vit B1 ( P for trend = 0.002), whereas Vit B61, B62, and B12 remained stable (all P -trend > 0.05). This pattern was echoed across multiple neuroendocrine pathways, with GDF15, SP, AVP, 5-HT, and ghrelin all uniformly decreasing as cortisol increased (all P for trend < 0.05). In contrast, IGFBP7 and leptin displayed non-significant declining trends ( P for trend = 0.05 and 0.069, respectively). A significant positive association was recorded between cortisol levels and the severity of NVP, as measured by both PUQE and VAS scores ( P for trend < 0.001 for both). This trend was further reflected in the proportion of severe NVP cases, which rose from 22.73% in the lowest cortisol Q1 to 47.13% in the Q4 ( P for trend < 0.001). Conversely, higher cortisol was associated with a concomitant decline in GWB ( P for trend = 0.005). Quality of life, assessed via the NVPQoL scale, also deteriorated with increasing cortisol levels ( P for trend = 0.028), primarily driven by a significant trend toward greater life limitations ( P for trend = 0.032). In contrast, no statistically significant trends were noted for the fatigue, emotional, or physical subscales of the NVPQoL, or for the scores of the SAS and SDS across cortisol quartiles (all P for trend > 0.05). Table 1 . Comprehensive clinical and biochemical parameters of the included NVP participants according to quartile of cortisol Anthropometric parameters Age, year, mean (SD) 29.9 (4.29) 28.64 (4.52) 28.86 (4.81) 28.79 (3.57) 0.239 0.173 Height, cm, mean (SD) 161.17 (5.3) 161.34 (5.12) 161.67 (5.07) 160.97 (5.09) 0.65 0.794 Weight, kg, mean (SD) 56.06 (9.19) 55.26 (8.99) 56.22 (9.6) 55.74 (10.23) 0.88 0.971 BMI, kg/m2, mean (SD) 21.54 (3.34) 21.19 (2.82) 21.44 (3.03) 21.46 (3.46) 0.91 0.976 Normal weight, n (%) 67 (76.14) 76 (84.44) 66 (78.57) 65 (74.71) 0.405 0.603 Overweight, n (%) 17 (19.32) 13 (14.44) 16 (19.05) 19 (21.84) 0.644 0.513 Obesity, n (%) 4 (4.55) 1 (1.11) 2 (2.38) 3 (3.45) 0.559 0.803 SBP, mmHg, mean (SD) 106 (9.62) 107.44 (9.71) 107.61 (8.89) 107.72 (9.61) 0.524 0.276 DBP, mmHg, mean (SD) 68.59 (6.82) 69.84 (6.7) 71.5 (7.32) 72.21 (8.1) 0.01 0.001 Respirations, beat, mean (SD) 19.18 (1.37) 19.44 (1.5) 19.39 (1.49) 19.34 (1.47) 0.473 0.608 Pulse, beat, mean (SD) 78.13 (9.63) 78.79 (10.58) 80.37 (10.48) 83.92 (14.48) 0.039 <0.001 Age at menarche, year, mean (SD) 13.74 (1.76) 13.52 (1.81) 13.46 (1.11) 13.52 (0.89) 0.628 0.367 Menstrual cycle, day, mean (SD) 30.69 (5.71) 31.54 (5.77) 30.24 (4.55) 30.64 (4.63) 0.171 0.617 Gestational age, day, mean (SD) 64.14 (14.95) 64 (14.13) 62.95 (13.86) 64.56 (15.74) 0.936 0.879 Duration of NVP at recruitment, day, mean (SD) 21.02 (14.19) 19.16 (14.95) 29.63 (108.02) 20.26 (14.43) 0.378 0.911 Spouse’s age, year, mean (SD) 32.15 (5.27) 30.67 (5.11) 30.35 (4.36) 30.64 (4.34) 0.127 0.578 Smoking history, n (%) 1 (1.14) 3 (3.33) 1 (1.19) 3 (3.45) 0.586 0.502 Smoking history of husband, n (%) 41 (46.59) 32 (35.56) 47 (55.95) 38 (43.68) 0.059 0.629 Hepatic, renal and thyroid function measurements ALT, U/L, mean (SD) 10.34 (8.49) 11.31 (8.88) 10.38 (8.15) 14.71 (17.68) 0.257 0.015 AST, U/L, mean (SD) 16.5 (8.09) 16.14 (6.07) 17.26 (6.17) 21.21 (14.83) 0.118 <0.001 ALP, U/L, mean (SD 48.28 (10.1) 46.87 (13.3) 48.81 (13.65) 54.11 (15.2) 0.007 0.001 UREA, mmol/L, mean (SD) 3.06 (0.86) 2.97 (0.93) 3.03 (0.87) 3.5 (1.09) 0.002 0.001 CREA, μmol/L, mean (SD) 39.21 (7.85) 39.93 (7.91) 42.44 (7.58) 41.98 (8.93) 0.011 0.012 TSH, mIU/L, mean (SD) 0.86 (0.68) 0.8 (0.63) 0.89 (0.75) 0.68 (0.84) 0.013 0.130 FT3, pmol/L, mean (SD) 3.49 (0.84) 3.63 (0.68) 3.75 (0.71) 4.27 (1.72) 0.001 <0.001 FT4, pmol/L, mean (SD) 14.07 (2.69) 14.81 (2.78) 14.66 (1.96) 17.28 (5.72) <0.001 <0.001 Electrolyte levels K, mmol/L, mean (SD) 4.06 (0.48) 4.08 (0.46) 4.18 (0.59) 3.94 (0.52) 0.086 0.119 Na, mmol/L, mean (SD) 136.44 (3.12) 136.19 (3.34) 137.77 (2.95) 136.28 (3.11) 0.004 0.895 Cl, mmol/L, mean (SD) 103.64 (2.81) 103.53 (2.87) 103.51 (3.09) 101.73 (3.65) 0.002 <0.001 Ca, mmol/L, mean (SD) 2.24 (0.11) 2.23 (0.15) 2.3 (0.12) 2.32 (0.16) <0.001 <0.001 Mg, mmol/L, mean (SD) 0.8 (0.06) 0.81 (0.06) 0.83 (0.06) 0.82 (0.07) 0.046 0.024 P, mmol/L, mean (SD) 1.14 (0.19) 1.18 (0.33) 1.3 (0.54) 1.24 (0.28) 0.199 0.055 Fe, μmol/L, mean (SD) 17.03 (6.16) 18.21 (5.62) 19.03 (7.06) 17.74 (6.08) 0.154 0.514 Zn, μmol/L, mean (SD) 13.04 (2.65) 13.51 (3.25) 14.31 (3.37) 14.21 (3.37) 0.038 0.010 Vitamins Vit B1, μg/L, mean (SD) 2.08 (1.25) 2.03 (1.31) 1.82 (1.1) 1.57 (0.98) 0.001 0.002 Vit B61, nmol/L, mean (SD) 5.41 (7.29) 3.95 (3.91) 5.81 (6.93) 5.61 (7.76) 0.116 0.459 Vit B62, nmol/L, mean (SD) 14.9 (12.76) 13.04 (14.36) 15.19 (14.82) 14.54 (14.31) 0.095 0.894 Vit B12, pmol/L, mean (SD) 11.98 (8.26) 12.19 (7.2) 13.19 (7.87) 10.84 (6.24) 0.191 0.319 Neuroendocrine biomarkers GDF15, pg/ml, mean (SD) 1721.81 (3183.96) 1167.01 (2566.46) 622.01 (1039.59) 883.74 (1643.12) 0.012 0.015 SP, pg/ml, mean (SD) 421.98 (838.21) 237.73 (395.5) 172.98 (230.51) 231.53 (495.12) 0.018 0.036 AVP, pg/ml, mean (SD) 107.53 (169.9) 72.41 (97.57) 54.3 (47.7) 63.71 (52.08) 0.033 0.010 IGFBP7, ng/ml, mean (SD) 26.34 (41.81) 17.63 (29.04) 12.36 (13.28) 16.91 (26.36) 0.071 0.05 LEP, ng/ml, mean (SD) 28.78 (42.68) 20.88 (32.07) 14.46 (13.82) 20.05 (25.55) 0.091 0.069 5-HT, ng/ml, mean (SD) 723.84 (1396.21) 397.97 (654.04) 247.14 (232.63) 387.18 (844.99) 0.041 0.023 Ghrelin, ng/ml, mean (SD) 20.31 (29.57) 12.74 (14.33) 9.39 (6.78) 11.83 (18.52) 0.006 0.008 Quality of life measures 10.34 (8.49) 11.31 (8.88) 10.38 (8.15) 14.71 (17.68) 0.257 0.015 PUQE sore, mean (SD) 10.9 (2.05) 10.51 (2.029) 10.9 (2.42) 12.11 (2.18) <0.001 <0.001 Moderate NVP, n (%) 68 (77.27) 72 (80.00) 61 (72.62) 46 (52.87) <0.001 <0.001 Severe NVP, n (%) 20 (22.73) 18 (20.00) 23 (27.38) 41 (47.13) <0.001 <0.001 VAS sore, mean (SD) 6.7 (1.59) 6.42 (1.83) 6.87 (1.93) 7.85 (1.85) <0.001 <0.001 GWB sore, mean (SD) 3.98 (2.14) 4.02 (2.394) 3.6 (2.64) 3.07 (2.43) 0.025 0.005 NVPQoL sore, mean (SD) 136.34 (38.91) 142.16 (43.5) 133.93 (41.69) 151.31 (39.57) 0.018 0.028 Physical symptoms, sore, mean (SD) 46.09 (10.05) 48.12 (12.311) 46.33 (13.18) 49.41 (10.53) 0.134 0.099 Fatigue sore, mean (SD) 19.65 (6.54) 19.63 (6.829) 19.19 (6.62) 21.44 (6.51) 0.091 0.066 Emotions sore, mean (SD) 28.24 (10.95) 30.02 (11.905) 27.62 (11.6) 31.87 (11) 0.062 0.065 Limitations sore, mean (SD) 42.36 (16.66) 44.38 (17.781) 40.79 (18.33) 48.59 (17.71) 0.02 0.032 SAS sore, mean (SD) 45.31 (8.98) 44.16 (8.527) 42.8 (7.62) 43.23 (8.19) 0.426 0.090 SDS sore, mean (SD) 47.19 (8.65) 47.76 (9.375) 45.36 (8.81) 49.23 (8.57) 0.037 0.196 3.2 Linear Associations Between Cortisol and Clinical Parameters, Biochemical Measurements, and Quality of Life Scores Across the Quartiles of Cortisol To further elucidate the relationship between cortisol and the measured variables, univariate and multivariate linear regression analyses were performed ( Table 2 ). After adjusting for age and BMI, serum cortisol levels remained independently associated with both DBP (β = 0.200, P < 0.001) and pulse rate (β = 0.196, P < 0.001). A substantial link was detected between cortisol and hepatic function, demonstrating positive associations with ALT (β = 0.136, P = 0.012), AST (β = 0.194, P < 0.001), and ALP (β = 0.184, P = 0.001). Similarly, renal function markers, UREA and CREA, were positively correlated with cortisol (β = 0.222, P < 0.001 and β = 0.118, P = 0.028, respectively). Thyroid function exhibited a distinct pattern: cortisol was positively associated with FT3 (β = 0.275, P < 0.001) and FT4 (β = 0.350, P < 0.001), yet inversely associated with TSH (β = -0.112, P = 0.037). Electrolyte profiling revealed cortisol was positively correlated with Ca (β = 0.213, P < 0.001), Mg (β = 0.159, P = 0.004), and P (β = 0.127, P = 0.020), but inversely related to K (β = -0.148, P = 0.009) and Cl (β = -0.277, P < 0.001). Among micronutrients, Zn showed a positive association (β = 0.120, P = 0.026), whereas Vit B1 attested to a strong negative association (β = -0.192, P < 0.001). Several regulatory biomarkers were inversely related to cortisol, including GDF15 (β = -0.151, P = 0.005), SP (β = -0.149, P = 0.005), 5-HT (β = -0.131, P = 0.015), and ghrelin (β = -0.153, P = 0.004). Cortisol levels were strongly associated with clinical severity metrics. Positive associations were found with both PUQE (β = 0.246, P < 0.001) and VAS scores (β = 0.274, P < 0.001), while inverse relationships were noted for the GWB score (β = -0.128, P = 0.030) and a positive association with the (worse) NVPQoL total score (β = 0.128, P = 0.015). A significant positive association was also found with the SDS depression score (β = 0.109, P = 0.042), though no such association was recorded with the SAS anxiety score ( P > 0.05). Table 2. Linear associations between cortisol and clinical and biochemical parameters β P - value β P - value Age (year) -0.071 0.184 - - BMI (kg/m2) -0.027 0.621 - - SBP (mmHg) 0.079 0.141 0.091 0.078 DBP (mmHg) * 0.191 <0.001 0.2 <0.001 Respirations (beat) * 0.081 0.130 0.079 0.144 Pulse (beat) * 0.196 <0.001 0.196 <0.001 Age at menarche (year) * -0.018 0.742 -0.016 0.765 Mean menstrual cycle (day) * -0.016 0.772 -0.02 0.71 Gestational age (day) * -0.029 0.584 -0.024 0.652 Duration of NVP at recruitment (day) * -0.030 0.571 -0.028 0.605 ALT (U/L) * 0.137 0.012 0.136 0.012 AST (U/L) * 0.192 <0.001 0.194 <0.001 ALP (U/L) * 0.182 0.001 0.184 0.001 UREA (mmol/L) * 0.197 <0.001 0.222 <0.001 CREA (μmol/L) * 0.114 0.036 0.118 0.028 TSH (mIU/L) * -0.11 0.04 -0.112 0.037 FT3 (pmol/L) * 0.278 <0.001 0.275 <0.001 FT4 (pmol/L) * 0.35 <0.001 0.35 <0.001 K (mmol/L) * -0.142 0.012 -0.148 0.009 Na (mmol/L) * -0.023 0.669 -0.022 0.685 Cl (mmol/L) * -0.279 <0.001 -0.277 <0.001 Ca (mmol/L) * 0.22 <0.001 0.213 <0.001 Mg (mmol/L) * 0.159 0.004 0.159 0.004 P (mmol/L) * 0.131 0.016 0.127 0.02 Fe (μmol/L) * -0.031 0.575 -0.038 0.489 Zn (μmol/L) * 0.122 0.023 0.12 0.026 Vit B1 (μg/L) * -0.198 <0.001 -0.192 <0.001 Vit B61 (nmol/L) * 0.028 0.604 0.031 0.564 Vit B62 (nmol/L) * -0.007 0.891 -0.004 0.937 Vit B12 (pmol/L) * 0.004 0.946 0.006 0.911 GDF15 (pg/ml) * -0.147 0.006 -0.151 0.005 SP (pg/ml) * -0.147 0.006 -0.149 0.005 AVP (pg/ml) * -0.089 0.099 -0.094 0.079 IGFBP7 (ng/ml) * -0.081 0.132 -0.087 0.104 LEP (ng/ml) * -0.062 0.248 -0.068 0.208 5-HT (ng/ml) * -0.129 0.016 -0.131 0.015 Ghrelin (ng/ml) * -0.148 0.006 -0.153 0.004 PUQE sore * 0.240 <0.001 0.246 <0.001 VAS sore * 0.271 <0.001 0.274 <0.001 GWB sore * -0.125 0.033 -0.128 0.03 NVPQoL sore * 0.125 0.019 0.128 0.015 SAS sore * -0.031 0.563 -0.026 0.626 SDS sore * 0.107 0.046 0.109 0.042 Note: a Adjusting for age and BMI; * Log-transformed. 3.3 Cortisol as an Independent Risk Factor for Severe NVP Logistic regression analyses were employed to evaluate the independent association between cortisol levels and the risk of severe NVP in Table 3 . A significant gradient of increasing risk was detected across cortisol quartiles in the unadjusted model ( P for trend < 0.001), where Q4 was associated with a three-fold higher risk of severe NVP compared to Q1 (OR = 3.03, 95% CI: 1.58–5.82, P < 0.01). This association was strengthened after adjusting for blood pressure in Model 2 (OR = 3.44, 95% CI: 1.76–6.74; P < 0.001). Crucially, in the fully adjusted Model 3 that accounted for a extensive set of confounders—including hemodynamic, hepatic, renal, thyroid, electrolyte, nutritional, and neuroendocrine factors—the association remained robust in Q4 versus Q1 (OR = 2.62, 95% CI: 1.23–5.66, P < 0.05), with a persistent significant trend ( P for trend < 0.001). Table 3. Adjusted OR (95% CI) for the associations between the CMI and the risk of Severe NVP. Model 1 1.00 (Reference) 0.85 (0.42, 1.74) 1.28 (0.64, 2.56) 3.03 (1.58, 5.82) ** <0.001 Model 2 1.00 (Reference) 0.87 (0.42, 1.80) 1.40 (0.70, 2.83) 3.44 (1.76, 6.74) *** <0.001 Model 3 1.00 (Reference) 0.91 (0.42, 1.96) 1.37 (0.62, 3.00) 2.62 (1.23, 5.66) * <0.001 Note: * P <0.05; ** P <0.01; *** P <0.001. Model 1: adjusted for no confounding factors; Model 2, adjusted for SBP and DBP; Model 3, adjusted for SBP, DBP, ALP, UREA, CREA, TSH、FT3、FT4, Ca, Cl, Vit B1, GDF15 and ghrelin; 3.4 Predictive Performance of Cortisol for Severe NVP To evaluate the biomarker potential of serum cortisol for severe NVP, a ROC analysis was performed ( Figure 2 ). The AUC was 0.62 (95% CI: 0.56–0.69; P < 0.001), indicating a significant yet modest predictive value. An optimal cortisol cutoff was determined at 14.55 μg/dL, yielding a specificity of 77.7% and a sensitivity of 47.1%. Figure 2. The results of ROC curve analysis regarding the predictability of cortisol in Severe NVP. 3.4 Multivariate Logistic Regression Analysis of Factors Associated with NVP Severity A stratified logistic regression model was constructed to identify independent factors associated with NVP severity ( Table 4 ). In the multivariable model adjusted for demographic and biochemical confounders, cortisol level (OR = 1.070, 95% CI: 1.022–1.120, P = 0.004) remained significantly and independently associated with an increased risk of severe NVP. Conversely, serum Fe (OR = 0.945, 95% CI: 0.903–0.988, P = 0.012) and serum K (OR = 0.526, 95% CI: 0.284–0.974, P = 0.041) emerged as significant protective factors. In addition, ALP was also positively associated with NVP severity (OR = 1.023, 95% CI: 1.001–1.046, P = 0.039). Table 4 . Multivariate Logistic regression analysis of factors affecting NVP Severity in NVP participants Age (year) 0.063 0.034 3.433 0.064 1.07 (1, 1.14) BMI (kg/m2) -0.044 0.045 0.952 0.329 0.96 (0.88, 1.05) Gestational age (day) 0 0.01 0.001 0.979 1 (0.98, 1.02) ALP (U/L) 0.023 0.011 4.173 0.041 1.02 (1, 1.05) UREA (mmol/L) 0.148 0.15 0.97 0.325 1.16 (0.86, 1.56) K (mmol/L) -0.639 0.315 4.124 0.042 0.53 (0.29, 0.98) Fe (μmol/L) -0.057 0.023 6.355 0.012 0.94 (0.9, 0.99) cortisol (ug/dL) 0.068 0.023 8.465 0.004 1.07 (1.02, 1.12) Doxylamine–pyridoxine 0.068 0.277 0.061 0.805 1.07 (0.62, 1.84) Acupuncture -0.248 0.278 0.79 0.374 0.78 (0.45, 1.35) 3.5 Analysis of Cortisol Levels Before and After Treatment The impact of therapeutic interventions on serum cortisol levels was evaluated across the entire cohort and within specific treatment groups ( Table 5 ). A significant and consistent decline in cortisol was noted following treatment. In the overall cohort, mean serum cortisol decreased significantly from 12.91 μg/dL (SD = 6.29) at baseline to 10.57 μg/dL (SD = 4.28) post-treatment, yielding a mean reduction of 2.34 μg/dL (95% CI: 1.55–3.09, P < 0.001). This effect was uniformly significant across all four intervention subgroups ( P < 0.05 for all). Table 5 . Analysis of cortisol changes before and after treatment All subjects Pre-Treatment 12.91 (6.29) 0.39 (1.55, 3.09) 5.93 <0.001 Post-Treatment 10.57 (4.28) Active acupuncture plus doxylamine–pyridoxine Pre-Treatment 13.14 (6.98) 0.88 (0.96, 4.47) 3.08 0.003 Post-Treatment 10.42 (4.9) Sham acupuncture plus doxylamine–pyridoxine Pre-Treatment 12.92 (5.87) 0.63 (1.27, 3.79) 4.01 <0.001 Post-Treatment 10.39 (4.25) Active acupuncture plus placebo Pre-Treatment 12.53 (5.58) 0.76 (0.1, 3.15) 2.13 0.037 Post-Treatment 10.9 (4.14) Sham acupuncture plus placebo Pre-Treatment 13.04 (6.75) 0.86 (0.67, 4.09) 2.77 0.007 Post-Treatment 10.66 (3.75) 3.6 Association Between Cortisol and NVP Severity Stratified by Treatment To investigate whether the association between cortisol levels and severe NVP was modified by therapeutic regimens, we conducted subgroup analyses using logistic regression within each treatment group ( Table 6 ). A pronounced and consistent positive association was presented when stratifying by primary treatment modality. Each 1 μg/dL increase in cortisol was associated with an 8% and 10% higher odd of severe NVP in the doxylamine–pyridoxine (OR = 1.08, 95% CI: 1.04–1.12, P < 0.001) and placebo (OR = 1.10, 95% CI: 1.04–1.16, P = 0.001) groups, respectively. A similar pattern was occurred when stratifying by acupuncture type. However, analysis of the combined treatment arms demonstrated a critical nuance: while cortisol persisted as a significant risk factor within all Placebo subgroups, its effect was substantially attenuated and was no longer a significant predictor within the doxylamine-pyridoxine subgroups, regardless of concomitant acupuncture (all P > 0.05). Table 6 . Analysis of cortisol c affecting NVP severity in different treatment Treatment group Doxylamine–pyridoxine 0.078 0.019 16.113 <0.001 1.08 (1.04, 1.12) Placebo 0.096 0.028 11.455 0.001 1.1 (1.04, 1.16) Active acupuncture 0.072 0.026 7.485 0.006 1.08 (1.02, 1.13) Sham acupuncture 0.084 0.029 8.736 0.003 1.09 (1.03, 1.15) subgroup Doxylamine–pyridoxine Active acupuncture 0.062 0.035 3.097 0.078 1.06 (0.99, 1.14) Sham acupuncture 0.058 0.04 2.14 0.143 1.06 (0.98, 1.15) Placebo Active acupuncture 0.084 0.039 4.64 0.031 1.09 (1.01, 1.17) Sham acupuncture 0.111 0.043 6.741 0.009 1.12 (1.03, 1.22) 3.7 Subgroup Analysis of the Association Between Cortisol and Severe NVP To evaluate the consistency of the association between cortisol levels and severe NVP risk across key patient demographics, we conducted subgroup analyses stratified by age, BMI, and gestational age ( Table 7 ). The analysis revealed persuasive evidence for a consistent positive association. Significantly elevated risks were observed in several clinically relevant strata, including participants aged ≥ 29 years (OR for Q4 = 3.48, 95% CI: 1.47–8.24, P = 0.005), those with normal BMI (OR for Q4 = 3.03, 95% CI: 1.46–6.32, P = 0.003), and those with a gestational age ≥ 62 days (OR for Q4 = 3.96, 95% CI: 1.45–10.79, P = 0.007). No significant interaction was detected for age ( P -interaction = 0.374), BMI ( P -interaction = 0.373), or gestational age ( P -interaction = 0.078). Table 7 . Analysis of cortisol affecting NVP severity in different subgroups Q1 Reference Age 0.374 < 29 years Cortisol Q2 0.51 (0.16, 1.64) 0.259 Cortisol Q3 1.54 (0.53, 4.44) 0.424 Cortisol Q4 2.59 (0.95, 7.07) 0.064 ≥ 29 years Cortisol Q2 1.29 (0.51, 3.26) 0.59 Cortisol Q3 1.11 (0.44, 2.78) 0.822 Cortisol Q4 3.48 (1.47, 8.24) 0.005 BMI 0.373 Normal Cortisol Q2 0.61 (0.27, 1.37) 0.228 Cortisol Q3 1.19 (0.55, 2.56) 0.658 Cortisol Q4 3.03 (1.46, 6.32) 0.003 Overweight Cortisol Q2 4.69 (0.74, 29.83) 0.102 Cortisol Q3 2.5 (0.39, 16.05) 0.334 Cortisol Q4 4.38 (0.76, 25.06) 0.097 Obesity Cortisol Q2 † - - Cortisol Q3 † - - Cortisol Q4 1.5 (0.06, 40.63) 0.81 Gestational age 0.078 < 62 days Cortisol Q2 0.51 (0.18, 1.4) 0.188 Cortisol Q3 0.55 (0.21, 1.48) 0.239 Cortisol Q4 2.37 (0.97, 5.79) 0.059 ≥ 62 days Cortisol Q2 1.5 (0.52, 4.35) 0.452 Cortisol Q3 3.2 (1.14, 9.02) 0.028 Cortisol Q4 3.96 (1.45, 10.79) 0.007 Note: † Due to the small sample size in the obese subgroup, some effect sizes could not be reliably estimated. 4. Discussion Our secondary analysis of the large NVPAct trial demonstrate elevated serum cortisol as an independent risk factor for severe NVP and reduced quality of life, concurrent with multisystem physiological alterations. Crucially, effective treatment significantly reduced cortisol levels, suggesting a modifiable component. These findings provide novel mechanistic insights by directly implicating HPA axis dysregulation in NVP pathophysiology. This study showed serum cortisol levels as being robustly associated with NVP severity in a dose-dependent manner. Ascending cortisol quartiles were correlated with significant increases in PUQE and VAS scores ( P for trend < 0.001), concomitant with deteriorations in NVPQoL and GWB scores. Linear regression confirmed that cortisol was independently associated with PUQE (β = 0.246, P < 0.001) and VAS scores (β = 0.274, P < 0.001) after adjusting for age and BMI. Crucially, multivariate logistic regression demonstrated that individuals in the highest cortisol quartile faced a 1.62-fold increased risk of severe NVP (95% CI: 1.23–5.66), a finding that persisted after adjustment for confounders including hemodynamic, metabolic, and neuroendocrine parameters. Furthermore, each 1 μg/dL increase in cortisol was associated with a 7% elevated risk of severe NVP (OR = 1.070, 95% CI: 1.022–1.120), solidifying cortisol’s role as an independent determinant of disease severity. This dose-response relationship provides mechanistic support for theoretical models positing a bidirectional stress-discomfort interplay in pregnancy. Pregnancy itself activates the HPA axis, while NVP symptoms constitute a persistent stressor that may further stimulate cortisol release[19, 20]. Our results supported a vicious cycle wherein severe NVP elevates cortisol, which may then exacerbate symptoms, creating a positive feedback loop. A existing study reported that pregnant women with NVP symptoms elucidated higher plasma cortisol levels from the first to the third trimester, but did not thoroughly explore the relationship with symptom severity[21]. Utilizing a large sample size and comprehensive multivariate adjustment, our study was the first to systematically highlight a dose-response relationship between cortisol levels and NVP severity in a Chinese population. We indicated concerted elevations in liver enzymes (ALT, AST, ALP) and renal markers (UREA, CREA) increased with rising cortisol levels, potentially reflecting the systemic metabolic impact of a hypercortisolemic state. Cortisol promotes gluconeogenesis and protein catabolism, processes that may augment hepatic metabolic load and modulate renal filtration and reabsorption function[22, 23]. Notably, ALP was independently associated with NVP severity in the multivariate model (OR = 1.023). Given its known placental origin during pregnancy, this association suggests a potential link between placental function and NVP pathophysiology, a finding that merits further investigation. Regarding thyroid function, we found strong positive correlations of cortisol with FT3 and FT4, alongside a weak inverse association with TSH. A more plausible explanation involves pregnancy-specific physiology: high hCG in early gestation can stimulate the thyroid, leading to gestational transient thyrotoxicosis, which is frequently presented in severe NVP[24]. Consequently, the elevated FT3 and FT4 levels likely reflect hCG-driven stimulation. The concurrence of high cortisol and thyroid hormones may represent a distinct endocrine milieu predisposing to severe NVP, warranting further study to disentangle their complex interplay. Electrolyte dysregulation represents a key pathophysiological feature of NVP[25]. Our analyses revealed distinct patterns of association with cortisol levels: positive for Ca, Mg, and P, but inverse for Cl and K. Cortisol possesses mild mineralocorticoid activity, which can influence electrolyte reabsorption in the renal tubules[26]. While vomiting commonly contributes to electrolyte imbalances in NVP, our findings indicated that a hypercortisolemic state may directly participate in or exacerbate these disturbances, independently of vomiting. Clinically, hypokalemia can cause muscle weakness, and cardiac arrhythmias, potentially worsening fatigue and discomfort in NVP patients[27]. The identification of serum K as a protective factor against severe NVP (OR = 0.526) further underscores the significance of electrolyte balance in NVP symptomatology. Moreover, Zn displayed a paradoxical positive association with cortisol, possibly reflecting stress-related redistribution or appetite-related metabolic shifts. In contrast, higher Fe levels were protective against severe NVP (OR = 0.945), likely serving as a marker of better baseline nutritional status rather than a direct cortisol-mediated effect. These findings uncovered the complex interplay between endocrine stress responses and micronutrient metabolism in NVP. A considerable negative association with cortisol was uniquely discovered for vit B1 among the vitamins analyzed. Vit B1 deficiency is not uncommon in pregnant women with persistent vomiting and can progress to Wernicke’s encephalopathy—a life-threatening complication—in severe cases[28]. Our results supported that a hypercortisolemic state may contribute to the depletion or a disrupt the distribution of this vitamin, underscoring a crucial consideration for preventing severe neurological complications in NVP. At the neuroendocrine level, a cohort of biomarkers central to nausea, vomiting, and appetite regulation—including GDF15, SP, 5-HT, and ghrelin—all showed significant inverse correlations with cortisol. This constitutes a noteworthy finding. GDF15 has recently been proved as a key mediator of pregnancy-related nausea and HG; its levels are elevated in pregnancy and it induces symptoms by activating the GDNF family receptor α–like in the brainstem’s area postrema[29]. Ghrelin, known as the ”hunger hormone,” stimulates appetite. Both 5-HT and SP are well-established neurotransmitters closely associated with the vomiting reflex[30]. We proposed that this apparent contradiction—wherein levels of typically pro-emetic or orexigenic factors are lower in the high-cortisol group—may be explained by a compensatory mechanism or receptor downregulation in an attempt to counteract the severe NVP state. In the context of sustained high cortisol and severe NVP, the body may mount a counter-regulatory response, attempting to mitigate symptoms by reducing the production of key pro-emetic signals. Alternatively, cortisol may directly suppress the expression of these neuroendocrine factors, a phenomenon documented by the known inhibitory effects of glucocorticoids on various cytokines and neuropeptides[31]. The intricate and occasionally opposing relationships observed in this study position cortisol as a key upstream regulator within a complex network. This finding underlines the need for future mechanistic studies to delineate the precise causal pathways involved. ROC curve analysis confirmed that cortisol possesses a statistically significant but moderate discriminatory power for predicting severe NVP, with an AUC of 0.62. This reinforces that while cortisol alone may be suboptimal as a standalone screening tool for severe NVP, it could serve as a valuable component within a comprehensive prediction model integrating clinical features and other biochemical parameters. Especially, serum cortisol levels decreased significantly following the intervention across all participants, irrespective of the specific treatment received, consistent with prior reports[32, 33]. Collectively, these findings imply that effective NVP therapies alleviate symptoms in part by attenuating HPA axis overactivation, with the subsequent feedback-mediated normalization of cortisol levels serving as an objective biomarker for treatment efficacy. Further stratified analysis revealed a critical effect modification by treatment type. The cortisol-severity association remained robust across all placebo subgroups but was attenuated and lost statistical significance in patients receiving doxylamine-pyridoxine. This suggests that effective pharmacological intervention may mitigate the pathological impact of high cortisol, thereby disrupting its link to severe NVP. This forms a hypothesis worthy of further investigation, prompting future studies to explore how pharmacological intervention modulates HPA axis activity and its downstream effects. Subgroup analyses confirmed that the positive association between elevated cortisol and severe NVP was robust and homogeneous across strata defined by age, BMI, and gestational age, as no significant interaction effects were detected. This consistency underscores the generalizability of cortisol’s role in NVP pathophysiology. Interestingly, the association was particularly striking among women with a gestational age ≥62 days and those aged ≥29 years, emphasizing that cortisol monitoring may hold enhanced clinical relevance in these specific populations. Strengths and limitations The major strengths of this study include its foundation in a large, rigorous RCT, the comprehensive profiling of a wide range of physiological parameters, and the analysis of pre- and post-treatment cortisol dynamics. Several limitations of this study warrant consideration. Firstly, as a secondary analysis of an RCT, the observed associations are inherently limited in establishing causality. Although we adjusted for an extensive array of potential confounders, residual confounding cannot be fully ruled out. Secondly, the assessment of cortisol at a single time point precludes insight into its diurnal variation or long-term secretory patterns; future studies would benefit from repeated sampling or salivary cortisol profiling to more accurately characterize HPA axis activity. Finally, our findings are derived from a cohort of Chinese women with moderate-to-severe NVP, and their generalizability to other ethnic populations or to individuals with mild symptoms requires further validation. 5. Conclusion In conclusion, this study redefines serum cortisol not merely as a stress marker, but as a pivotal, independent risk factor and a treatment-responsive biomarker in NVP. Its association with multisystem alterations implies it acts as a pathophysiological hub, integrating endocrine stress with the metabolic and neuroendocrine disturbances of the disease. The consistent decrease after effective treatment confirms its role as a modifiable central driver. CRediT authorship contribution statement Baichao Shi : Data curation, Formal analysis, Investigation, Writing - original draft; Hong Yu : Formal analysis, Validation, Writing- original draft; Zhihui Zhang , Fengjuan Lu , Muxin Guan , Jiannan Yu , Zhuwei Gao and Yue Gao : Writing - review and editing; Jiaxing Feng and Jing Cong : Investigation; Yu Wang : Writing - review and editing, Investigation; Xiaoke Wu : Conceptualization, Data curation, Methodology, Writing - review and editing. Ethical considerations The studies involving human participants were reviewed and approved by the Institutional Review Board of the First Affiliated Hospital of Heilongjiang University of Chinese Medicine (HZYLLKY201902301). It was also registered on ClinicalTrials.gov (NCT04401384). All participants provided written informed consent. All study data were de-identified, with participants identified only by participant IDs. Declaration of Generative AI and AI-assisted technologies in the writing process AI not used in the preparation of this work. Funding This work is supported by (1) The National key R&D Program of China (2019YFC1709500); (2) The National Collaboration Project of Critical Illness by Integrating Chinese Medicine and Western Medicine; (3) Project of Heilongjiang Province Innovation Team ”TouYan” (LH2019H046); (4) Heilongjiang Provincial Clinical Research Centre for Ovary Diseases (LC2020R009); (5) Traditional Chinese Medicine Research Project of Heilongjiang Administration of Traditional Chinese Medicine (ZHY2022-124); (6) The project of Evidence-based capacity in Traditional Chinese Medicine (TCM Sci-Tech Internal Letter [2023] No. 24). Declaration of Competing Interes t The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors sincerely thank all the pregnant women who participated in this study. We are also grateful to the clinical staff and research coordinators at each site for their invaluable assistance with recruitment and data collection. Finally, we gratefully acknowledge the principal investigators and team of the original RCT for providing the data for this secondary analysis. Data availability The datasets generated and/or analyzed during the current study are not publicly available due to restrictions designed to protect research participant privacy. However, they are available from the corresponding author upon reasonable request. Reference [1] Einarson TR, Piwko C, Koren G. Quantifying the global rates of nausea and vomiting of pregnancy: a meta analysis. 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Keywords developing countries: obstetrics and gynaecology diagnostic studies early pregnancy endocrinology epidemiology: general obstetric general obstetrics maternal physiology Authors Affiliations Baichao Shi 0000-0001-9559-4611 Heilongjiang University of Chinese Medicine View all articles by this author Hong Yu Zhejiang Provincial Hospital of Chinese Medicine View all articles by this author Zhihui Zhang Anjian Hospital View all articles by this author Fengjuan Lu Heilongjiang University of Chinese Medicine View all articles by this author Muxin Guan Heilongjiang University of Chinese Medicine View all articles by this author Jiannan Yu Heilongjiang University of Chinese Medicine View all articles by this author Zhuwei Gao Heilongjiang University of Chinese Medicine View all articles by this author Yue Gao Heilongjiang University of Chinese Medicine View all articles by this author Jiaxing Feng First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine View all articles by this author Jing Cong First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine View all articles by this author Yu Wang First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine View all articles by this author Metrics & Citations Metrics Article Usage 149 views 63 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Baichao Shi, Hong Yu, Zhihui Zhang, et al. Serum Cortisol as an Independent Biomarker for Disease Severity and a Modifiable Factor in Nausea and Vomiting of Pregnancy: A Secondary Analysis of a Randomized Controlled Trial. Authorea . 29 January 2026. DOI: https://doi.org/10.22541/au.176971690.09278552/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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