Effect of circadian rhythm on doppler parameters ın patients with gestational diabetes mellitus

Circadian rhythm (CR) refers to 24-hour biological and physiological cycles influenced by internal and external factors [1]. The term “circadian,” introduced by Franz Halberg in 1959, originates from the Latin words circa (about) and dies (day) and describes regular daily biological fluctuations [1]. The suprachiasmatic nucleus (SCN) in the anterior hypothalamus functions as the master clock, coordinating circadian processes throughout the body [2]. Peripheral circadian mechanisms are also present in many organs, including the placenta, which transmits rhythmic maternal signals and regulates fetal circadian biology through its own oscillatory functions. Pregnancy requires a highly coordinated interaction of metabolic, endocrine, and circadian systems [3]. Circadian mechanisms influence endocrine activity, metabolism, basal body temperature, and maternal behavior through melatonin regulation [4]. Previous studies have demonstrated that fetal biophysical profile (BPP) scores are higher in the evening than in the morning, indicating diurnal variation in fetal activity [5]. The maternal, placental, and fetal circadian systems interact dynamically; while maternal circadian adaptation supports fetal development, the fetus simultaneously establishes its own rhythm under the influence of maternal SCN signaling and melatonin [6]. Human metabolism exhibits circadian variation across multiple pathways, particularly in glucose and leptin metabolism. Melatonin and its metabolites exert potent antioxidant effects against reactive oxygen species generated during pregnancy, providing cellular and tissue-level protection. Impaired circadian rhythm gene expression increases the risk of stroke, myocardial infarction, metabolic and autoimmune disorders, depression, and anxiety, and contributes to the pathogenesis of gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM) [7–9]. Experimental and clinical studies further demonstrate that reduced melatonin secretion and disrupted circadian balance in women with impaired neuroimmune–endocrine function play a key role in the development of GDM, supporting the critical role of melatonin’s circadian release in maintaining healthy pregnancy and fetal outcomes [10]. Despite the well-established role of Doppler ultrasound in GDM surveillance, the potential influence of circadian rhythm on Doppler findings remains underexplored. Considering the impact of chronobiological factors on glycemic variability and vascular function, clarifying the relationship between circadian timing and Doppler parameters could enhance risk stratification and inform targeted management strategies in GDM pregnancies [11]. In this study, we investigated the effect of circadian rhythm on maternal and fetal Doppler parameters in patients who developed gestational diabetes mellitus during pregnancy.

This prospective cross-sectional study included 80 pregnant women diagnosed with insulin-regulated gestational diabetes mellitus (GDM), diet-regulated GDM, or normal pregnancies between 32 and 39 gestational weeks at the Perinatology Unit of Zeynep Kamil Women and Children’s Diseases Training and Research Hospital. Ethical approval was obtained (22.07.2020, No. 165). Participants were classified into three groups: insulin-regulated GDM, diet-regulated GDM, and controls. Diagnostic criteria followed the two-step 50-g and 100-g oral glucose tolerance tests (OGTT). Patients with two or more abnormal values were diagnosed with GDM. Fasting glucose < 95 mg/dL, 1-hour < 140 mg/dL, and 2-hour < 120 mg/dL indicated good glycemic control. Doppler measurements were obtained at sunrise (05:00–07:00) and sunset (20:00–22:00) using a Voluson E8 Color Doppler Ultrasonography device (2–8 MHz AB2-7-D wideband convex probe). Peak systolic velocity (PSV), systole/diastole ratio (S/D), pulsatility index (PI), and resistance index (RI) were measured for the umbilical artery (UA), middle cerebral artery (MCA), and right and left uterine arteries (rUtA, lUtA). All ultrasonographic examinations and measurements were performed by a single practitioner (ED) with Voluson E8 Color Doppler Ultrasonography device. 2–8 MHz AB2-7-D- Wideband convex probe was used in the examinations. For umbilical artery assessment, the free loop of the cord was used, avoiding fetal or placental proximity. The umbilical cord was examined for the presence of two arteries with one accompanying vein. For the assessment of both uterine arteries, the transducer was positioned longitudinally on the lateral aspect of the lower abdominal quadrant and directed medially. The Doppler sampling site was located approximately 1 cm inferior to the apparent crossover point of the uterine and external iliac arteries. For MCA evaluation, an axial section including the thalamus and sphenoid wings was obtained; the Doppler gate was placed in the proximal one-third of the vessel. The insonation angle was kept near 0° and below 15°, and measurements were recorded from 3–10 uniform cycles. To assess intraobserver variability, two midday (11:00–13:00) Doppler sessions were performed with a 30-minute interval in 20 randomly selected cases. Patients with multiple pregnancies, unregulated GDM, intrauterine growth restriction, or oligohydramnios were excluded. Statistical analysis The recorded measurements were analyzed using SPSS for Windows (Version 20.0; IBM Corp., Armonk, NY, USA). The Kolmogorov–Smirnov test was applied to assess data normality. Continuous variables were expressed as mean ± standard deviation, minimum, and maximum values. The Kruskal–Wallis test was used for comparisons among multiple groups, while the Wilcoxon signed-rank test and Pearson correlation analysis were applied for paired and correlation assessments, respectively. A p value < 0.05 was considered statistically significant. Intraobserver reliability was evaluated using the intraclass correlation coefficient (ICC) with a two-way mixed-effects model (absolute agreement, double measurement) and 95% confidence intervals. Intraclass correlation coefficient values 0.9 excellent reliability. A priori power analysis was conducted using G*Power software (version 3.1; Heinrich-Heine-Universität Düsseldorf, Germany) with an assumed effect size of 0.3, α = 0.05, and power (1–β) = 0.80, revealing that a minimum of 66 participants was required to achieve adequate statistical power.

A total of 80 pregnant women were included in the study, comprising 40 patients with gestational diabetes mellitus (GDM) and 40 healthy controls. Among the GDM group, blood glucose levels were regulated with insulin therapy in 24 patients and with dietary modification in 15 patients. One participant was excluded from the study due to the detection of intrauterine growth restriction. Maternal demographic and clinical characteristics were comparable among the control, diet-regulated GDM, and insulin-regulated GDM groups. Age, gravidity, parity, LMP, and gestational age were comparable across all groups (p > 0.05). The mean body mass index (BMI) was 29.16 ± 6.1 kg/m² in the control group, 28.67 ± 4.3 kg/m² in the diet-regulated GDM group, and 32.7 ± 5.7 kg/m² in the insulin-regulated GDM group, showing a statistically significant difference (p = 0.02). Hemoglobin A1c (HbA1c) levels were similar between the GDM subgroups, measured as 5.83 ± 0.3% in the diet-regulated and 6.17 ± 0.9% in the insulin-regulated GDM group (p = 0.4) (Tab. 1). | Table 1. Patient demographics | |||| | Control (n = 40) (mean ± SD, ± min–max) | Gestational diabetes mellitus (insulin) (n = 24) (mean ± SD ± min–max) | Gestational diabetes mellitus(diet) (n = 15) (mean ± SD, ± min–max) | p value | | | Age [year] | 31.1 ± 6.1 | 34.6 ± 6.6 | 33.8 ± 4.8 | 0.06 | | Gravida (n) | 3 (0–6) | 3 (0–5) | 3 (0–5) | 0.2 | | Parity (n) | 2 (0–4) | 2 (0–5) | 2 (0–5) | 0.3 | | BMI [kg/m2]* | 29.1 ± 6.1 | 32.7 ± 5.7 | 28.6 ± 4.3 | 0.02 | | LMP [weeks] | 34.2 ± 1.5 | 34.6 ± 1.8 | 33.8 ± 1.3 | 0.4 | | pUSG [weeks] | 34.3 ± 1.9 | 34.5 ± 1.8 | 33.9 ± 1.6 | 0.7 | | HmgA1c [%] | – | 6.1 ± 0.9 | 5.8 ± 0.3 | 0.4 | | *BMI measured at the time of enrollment; BMI — body mass index; GDM — gestational diabetes mellitus; LMP — last menstrual period; pUSG — perinatal ultrasound; SD — standard deviation | Middle cerebral artery Doppler measurements In the control group (n = 40), morning and evening MCA PSV and MCA RI values were comparable (p = 0.9, p = 0.06), whereas significant differences were observed in MCA S/D and MCA PI measurements (p = 0.03 and p = 0.01, respectively). In the insulin-regulated GDM group (n = 24), no significant differences were found in MCA PSV, MCA S/D, or MCA RI between morning and evening measurements (p = 0.62, p = 0.39, and p = 0.09), while MCA PI differed significantly (p = 0.006). In the diet-regulated GDM group (n = 15), no statistically significant differences were detected in MCA PSV, S/D, PI, or RI values between morning and evening evaluations (p = 0.86, p = 0.11, p = 0.24, and p = 0.07, respectively) (Tab. 2). | Table 2. Comparison of morning and evening middle cerebral artery (MCA) Doppler measurements | |||| | Group | Variable | Morning mean ± SD (min–max) | Evening mean ± SD (min–max) | p value | | Control | MCA PSV | 44.2 ± 14.0 (17.5–70.3) | 44.6 ± 12.9 (14.5–83.8) | 0.90 | | MCA SD | 5.0 ± 1.7 (2.4–9.0) | 4.6 ± 2.0 (2.3–13.0) | 0.03 | | | MCA PI | 1.8 ± 0.4 (0.9–3.2) | 1.6 ± 0.4 (0.9–2.6) | 0.01 | | | MCA RI | 0.8 ± 0.1 (0.5–1.0) | 0.7 ± 0.1 (0.6–1.0) | 0.06 | | | Insulin-regulated GDM | MCA PSV | 56.8 ± 54.0 (24.9–305.4) | 45.6 ± 12.5 (24.7–69.1) | 0.62 | | MCA SD | 5.7 ± 1.9 (2.3–10.5) | 5.2 ± 2.3 (2.1–11.5) | 0.39 | | | MCA PI | 1.0 ± 0.4 (0.9–2.8) | 1.6 ± 0.4 (0.8–2.4) | 0.006 | | | MCA RI | 0.8 ± 0.1 (0.5–1.0) | 0.7 ± 0.1 (0.5–1.0) | 0.09 | | | Diet-regulated GDM | MCA PSV | 51.0 ± 13.3 (38.3–89.5) | 49.3 ± 14.7 (31.5–80.5) | 0.86 | | MCA SD | 7.1 ± 2.7 (3.1–12.6) | 5.8 ± 3.9 (2.6–14.3) | 0.11 | | | MCA PI | 2.0 ± 0.3 (1.2–2.4) | 1.9 ± 0.7 (1.1–3.6) | 0.24 | | | MCA RI | 0.8 ± 0.07 (0.6–0.9) | 0.8 ± 0.1 (0.6–1.0) | 0.07 | | | GDM — gestational diabetes mellitus; MCA — middle cerebral artery; PI — pulsatility index; PSV — peak systolic velocity; RI — resistance index; SD — standard deviation | Umblical artery Doppler measurements No significant differences were observed between morning and evening measurements of Umb PSV, S/D, PI, or RI in the control, insulin-regulated GDM, or diet-regulated GDM groups (all p > 0.05) (Tab. 3). | Table 3. Comparison of morning and evening umblical artery (UA) Doppler measurements | |||| | Group | Variable | Morning mean ± SD (min–max) | Evening mean ± SD (min–max) | p value | | Control | Umb PSV | 43.6 ± 11.0 (26.5–83.3) | 46.4 ± 12.2 (26.8–78.0) | 0.34 | | Umb SD | 2.9 ± 2.6 (1.7–18.8) | 2.5 ± 0.4 (1.8–3.9) | 0.87 | | | Umb PI | 0.9 ± 0.2 (0.5–1.5) | 0.9 ± 0.1 (0.6–1.4) | 0.66 | | | Umb RI | 0.6 ± 0.3 (0.4–2.9) | 0.5 ± 0.07 (0.4–0.7) | 0.77 | | | Insulin-regulated GDM | Umb PSV | 45.1 ± 10.4 (25.4–62.4) | 44.9 ± 11.0 (29.7–68.7) | 0.64 | | Umb SD | 2.7 ± 0.5 (1.9–4.0) | 2.5 ± 0.6 (1.7–4.4) | 0.18 | | | Umb PI | 0.9 ± 0.1 (0.6–1.3) | 0.9 ± 0.2 (0.6–1.4) | 0.24 | | | Umb RI | 0.6 ± 0.07 (0.4–0.7) | 0.5 ± 0.08 (0.4–0.8) | 0.12 | | | Diet-regulated GDM | Umb PSV | 39.4 ± 11.3 (22.4–60.2) | 42.6 ± 12.6 (22.5–67.0) | 0.49 | | Umb SD | 2.8 ± 0.4 (2.2–3.6) | 3.5 ± 3.3 (1.9–15.5) | 0.73 | | | Umb PI | 1.0 ± 0.1 (0.7–1.2) | 1.0 ± 0.2 (0.7–1.9) | 0.50 | | | Umb RI | 0.6 ± 0.05 (0.5–0.7) | 0.6 ± 0.1 (0.5–0.9) | 0.87 | | | GDM — gestational diabetes mellitus; SD — standard deviation; Umb PI — umbilical artery pulsatility index; Umb PSV — umbilical artery peak systolic velocity; Umb RI — umbilical artery resistance index; Umb SD — umbilical artery systole/diastole velocity ratio | Uterine arteries Doppler measurements In the control and diet-regulated GDM groups, no significant differences were observed between morning and evening rUtA PSV, S/D, PI, or RI measurements (all p > 0.05). In the insulin-regulated GDM group (n = 24), rUtA PSV values did not differ significantly between morning and evening (p = 0.71); however, rUtA S/D, PI, and RI showed significant variations (p = 0.014, p = 0.008, and p = 0.001, respectively). No significant differences were detected in lUtA PSV, S/D, PI, or RI parameters across morning and evening measurements in any group (all p > 0.05). | Table 4. Comparison of morning and evening right (rUt) and left uterine (lUt) artery Doppler measurements | ||||||| | Group | Variable | Right uterine — morning (mean ± SD, min–max) | Right uterine — evening (mean ± SD, min–max) | p value | Left uterine — morning (mean ± SD, min–max) | Left uterine — evening (mean ± SD, min–max) | p value | | Control | PSV | 70.1 ± 24.7-24.7–130.6) | 72.6 ± 39.1 (22.7–219.4) | 0.93 | 70.1 ± 32.1 (36.3–206.4) | 73.0 ± 33.3 (33.2–185.0) | 0.74 | | SD | 2.3 ± 0.8 (1.4–6.3) | 2.2 ± 0.5 (1.5–3.6) | 0.74 | 2.9 ± 3.7 (1.7–26.0) | 5.2 ± 17.8 (1.6–115.1) | 0.62 | | | PI | 0.9 ± 0.3 (0.4–2.5) | 0.9 ± 0.3 (0.4–1.8) | 0.52 | 1.0 ± 0.4 (0.6–2.8) | 0.9 ± 0.4 (0.5–2.5) | 0.46 | | | RI | 0.5 ± 0.1 (0.3–0.8) | 0.5 ± 0.1 (0.3–0.7) | 0.54 | 0.5 ± 0.1 (0.4–0.9) | 0.5 ± 0.1 (0.4–0.9) | 0.35 | | | Insulin-regulated GDM | PSV | 66.8 ± 19.8 (34.7–104.0) | 72.8 ± 33.9 (27.8–152.1) | 0.71 | 63.2 ± 23.8 (31.2–111.7) | 70.4 ± 34.4 (32.6–148.9) | 0.20 | | SD | 2.2 ± 1.0 (1.4–7.0) | 2.5 ± 0.7 (1.8–4.9) | 0.014 | 2.4 ± 1.0 (1.5–5.1) | 2.6 ± 1.0 (1.4–6.0) | 0.25 | | | PI | 0.8 ± 0.3 (0.4–2.2) | 1.0 ± 0.3 (0.6–2.0) | 0.008 | 0.9 ± 0.4 (0.5–2.0) | 1.1 ± 0.5 (0.4–3.0) | 0.06 | | | RI | 0.5 ± 0.1 (0.3–0.8) | 0.6 ± 0.2 (0.4–1.8) | 0.001 | 0.5 ± 0.1 (0.3–0.8) | 0.5 ± 0.1 (0.3–0.8) | 0.11 | | | Diet-regulated GDM | PSV | 71.6 ± 30.0 (25.0–148.7) | 58.2 ± 24.9 (27.9–116.1) | 0.07 | 79.5 ± 34.2 (40.6–143.8) | 74.8 ± 37.4 (29.1–137.9) | 0.73 | | SD | 2.6 ± 1.3 (1.6–6.9) | 2.7 ± 1.6 (1.6–8.1) | 0.95 | 2.3 ± 0.9 (1.5–5.5) | 2.2 ± 0.6 (1.6–4.1) | 0.43 | | | PI | 1.0 ± 0.5 (0.5–2.8) | 1.0 ± 0.5 (0.6–2.5) | 0.82 | 0.9 ± 0.3 (0.5–2.1) | 0.9 ± 0.2 (0.5–1.6) | 0.68 | | | RI | 0.5 ± 0.1 (0.3–0.8) | 0.5 ± 0.1 (0.4–0.9) | 0.97 | 0.5 ± 0.1 (0.3–0.8) | 0.5 ± 0.09 (0.4–0.8) | 0.47 | | | GDM — gestational diabetes mellitus; MCA — middle cerebral artery; PI — pulsatility index; PSV — peak systolic velocity; RI — resistance index; SD — systole/diastole velocity ratio; Umb — umbilical artery | Intraobserver variability analysis Intraclass correlation coefficient (ICC) analysis revealed moderate reliability for umbilical artery (UA) PSV (0.59) and S/D (0.63), and good reliability for PI (0.80) and RI (0.77). For the middle cerebral artery (MCA), ICC values indicated consistently good reliability for PSV, S/D, PI, and RI (0.59, 0.68, 0.67, and 0.67, respectively). The right uterine artery (rUtA) demonstrated low reliability for PSV (0.40) but very good reliability for S/D, PI, and RI (0.93, 0.90, and 0.89, respectively). The left uterine artery (lUtA) showed poor reliability for PSV (0.16), while S/D, PI, and RI values exhibited good to very good reliability (0.76, 0.80, and 0.73, respectively). Overall, ICC analysis demonstrated acceptable intraobserver consistency across most Doppler parameters, supporting the reproducibility of the measurements performed. | Table 5. Intraclass correlation coefficients (ICC) for Doppler parameters of the umbilical, middle cerebral, and uterine arteries | |||||| | Artery/parameter | ICC (single) | 95% CI (min–max) | ICC (mean) | 95% CI (min–max) | F value | p value | | Umbilical PSV | 0.41 | –0.02–0.72 | 0.59 | –0.05–0.83 | 2.38 | 0.033 | | Umbilical S/D | 0.46 | 0.05–0.74 | 0.68 | 0.09–0.85 | 2.75 | 0.016 | | Umbilical PI | 0.66 | 0.33–0.85 | 0.80 | 0.50–0.92 | 4.92 | 0.001 | | Umbilical RI | 0.63 | 0.27–0.83 | 0.77 | 0.42–0.91 | 4.30 | 0.001 | | MCA PSV | 0.42 | –0.02–0.72 | 0.59 | –0.04–0.84 | 2.41 | 0.031 | | MCA S/D | 0.52 | 0.06–0.79 | 0.68 | 0.11–0.88 | 3.09 | 0.015 | | MCA PI | 0.51 | 0.10–0.77 | 0.67 | 0.22–0.87 | 3.32 | 0.006 | | rUt PSV | 0.25 | –0.20–0.62 | 0.40 | –0.51–0.76 | 1.66 | 0.137 | | rUt S/D | 0.93 | 0.83–0.97 | 0.87 | 0.72–0.95 | 15.14 | < 0.001 | | rUt PI | 0.82 | 0.61–0.92 | 0.90 | 0.76–0.96 | 10.18 | < 0.001 | | rUt RI | 0.89 | 0.73–0.95 | 0.80 | 0.57–0.91 | 9.10 | < 0.001 | | lUt PSV | –0.07 | –0.49–0.37 | –0.16 | –1.94–0.54 | 0.86 | 0.626 | | lUt S/D | 0.62 | 0.24–0.83 | 0.76 | 0.39–0.90 | 5.07 | < 0.001 | | lUt PI | 0.67 | 0.33–0.85 | 0.80 | 0.49–0.92 | 5.78 | < 0.001 | | lUt RI | 0.58 | 0.21–0.80 | 0.73 | 0.35–0.89 | 4.01 | 0.002 | | CI — cofidence interval; lUt — left uterine artery; MCA — middle cerebral artery; PI — pulsatility index; PSV — peak systolic velocity; RI — resistance index; rUt — right uterine artery; S/D — systole/diastole ratio; Umb — umbilical artery |

The mother, placenta, and fetus are distinct circadian entities that interact dynamically throughout pregnancy. Numerous mechanisms enable the mother to directly or indirectly influence the fetal biological clock and reinforce the fetal circadian rhythm. Current evidence indicates that fetal circadian activity is modulated primarily by maternal melatonin secretion, while maternal glucocorticoid levels may also affect fetal pituitary and adrenal function, resulting in measurable alterations in fetal parameters [12]. Under normal physiological conditions, maternal blood pressure follows a circadian pattern, remaining lower at night [13]. Similarly, fetal biophysical profile scores and fetal heart rate demonstrate diurnal rhythmicity, which underscores the importance of considering these temporal variations when planning fetal surveillance and interpreting clinical data [5, 14]. The routine implementation of Doppler ultrasonography to evaluate utero-placental and fetal circulatory hemodynamics in high-risk pregnancies is increasing, as these measurements provide critical insight into both maternal and fetal well-being [15]. The literature on the influence of diurnal rhythm on fetal and maternal Doppler indices remains limited. One of the earliest studies exploring this relationship was conducted by Walker et al. [16] in a sheep model, where the presence of circadian oscillations in umbilical and uterine arterial flow was demonstrated. However, subsequent human studies have yielded inconsistent results, with most failing to demonstrate clinically significant circadian changes in Doppler parameters. In a study involving 100 uncomplicated pregnancies between 20 and 39 weeks of gestation, significant diurnal variation in umbilical and middle cerebral artery (MCA) pulsatility index (PI) values was observed, though these fluctuations were not clinically meaningful [11]. Similarly, Avitan et al. [17] examined 68 women in the third trimester and found diurnal variation in fetal cerebral and umbilical blood flow independent of fetal movement and heart rate, but no comparable variation in uterine artery flow. In the present study, we investigated whether impaired circadian rhythm gene expression — known to contribute to the development of gestational diabetes mellitus affects maternal and fetal Doppler parameters. To our knowledge, this is the first study to evaluate diurnal variation in Doppler indices specifically among patients with GDM. When the demographic characteristics of the study population were analyzed, no significant differences were noted among the groups regarding maternal age, gravidity, parity, or gestational age. However, body mass index differed significantly, with mean values of 29.16 ± 6.1 in the control group, 28.67 ± 4.3 in the diet-regulated GDM group, and 32.7 ± 5.7 in the insulin-regulated GDM group (p = 0.02). Obesity is known to impair vascular compliance in non-pregnant women and is associated with increased atherosclerotic risk [18, 19]. During pregnancy, elevated BMI is linked to higher blood pressure, increased left ventricular workload, and augmented stroke volume [20]. Insufficient spiral artery remodeling in obese women may compromise low-resistance uteroplacental circulation, leading to altered Doppler indices [20]. Previous findings regarding the relationship between BMI and Doppler parameters have been inconclusive. Aksoy et al. found no significant differences in Doppler indices among normal-weight, overweight, and obese women, whereas a 2020 study demonstrated that the physiological decline in uterine artery PI values was attenuated in overweight (24.3%) and obese women (21.3%) compared with normal-weight women (25.7%) [21]. In our study, the right uterine artery (rUtA) Doppler parameters did not exhibit diurnal variation in the control or diet-regulated GDM groups. However, statistically significant morning–evening differences in S/D, PI, and RI values were observed in the insulin-regulated GDM group. Understanding uterine artery resistance requires consideration of uterine hemodynamics. In the non-pregnant state, uterine blood flow is approximately 100 mL/min but increases to nearly 700 mL/min at term. Roughly 80% of this flow supplies the intervillous space, while 20% perfuses the myometrium. In species with hemochorial placentation, uteroplacental blood flow is primarily governed by arterial resistance. Because these vessels are maximally dilated during pregnancy, autoregulation is minimal; thus, uterine perfusion largely depends on maternal cardiac output, uterine vascular resistance, and uteroplacental perfusion pressure [22]. The maternal microenvironment — comprising metabolic status, adiposity, hormonal milieu, cytokine activity, endothelial function, and adipokines — may be influenced by circadian mechanisms, although this relationship remains underexplored. Placental morphology in diabetic pregnancies often displays structural alterations that may compromise uteroplacental perfusion. In poorly controlled GDM, fluctuations in fetal plasma glucose levels are greater, resulting in increased fetal heart rate variability relative to non-diabetic pregnancies [23]. Although prior studies have not demonstrated significant differences in uterine artery PI values between diabetic and non-diabetic pregnancies, diabetic women tend to show increased systemic placental afterload, myocardial contractility, and heart rate, leading to elevated uterine artery peak systolic velocity (UtA PSV) compared with healthy controls [23, 24]. The absence of significant differences in PI values suggests that enhanced arterial compliance may increase PSV without altering afterload [23]. Given that vascular compliance is typically reduced in obesity, it remains unclear whether this adaptive response is disrupted in obese GDM patients. Notably, the potential diurnal variation of these hemodynamic adaptations has not been previously investigated [18, 19]. In our study, the insulin-regulated GDM group — characterized by a higher mean BMI (32.7 ± 5.7) — exhibited significant diurnal differences in rUtA S/D, PI, and RI measurements compared with other groups. Furthermore, BMI correlated significantly with evening rUtA PSV (r = −0.296, p = 0.008). To our knowledge, this represents the first documentation of such a relationship, though confirmation through larger-scale studies is warranted. Additionally, MCA PI demonstrated significant diurnal variation in both the control (p = 0.01) and insulin-regulated GDM (p = 0.006) groups, with morning values exceeding evening values. This finding aligns with previous studies of uncomplicated pregnancies, suggesting that insulin-treated diabetic pregnancies maintain a similar circadian hemodynamic pattern. The absence of meaningful differences in umbilical artery Doppler indices across groups supports prior evidence that the timing of umbilical artery Doppler assessments does not affect clinical management in GDM. Furthermore, intra-observer reliability analysis using intraclass correlation coefficients demonstrated at least moderate to excellent agreement for clinically relevant parameters (S/D, PI, and RI), confirming that observer variability did not influence the observed morning–evening differences.

When Doppler assessments are performed at short intervals, measurement results remain consistent; however, physiological fluctuations driven by diurnal rhythm may produce significant differences when evaluations are conducted at different times of the day. These findings emphasize that the influence of circadian variation should be taken into account when interpreting Doppler measurements, particularly in the monitoring and clinical management of complicated pregnancies. Article information and declarations Data availability statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. Ethics statement This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the local Ethics Committee of Zeynep Kamil Women and Children’s Diseases Training and Research Hospital. Written informed consent was obtained from all participants prior to enrollment in the study. Author contributions All authors contributed equally to the manuscript. Funding No external funding was used for this study. Acknowledgments None. Conflict of interest The authors declare that there is no conflict of interest regarding the publication of this paper. Supplementary material None.