A risk marker for pregnancy complications and its application

Abstract

This invention belongs to the field of biodetection technology and discloses a risk biomarker for pregnancy complications and its application. The invention provides a risk biomarker for pregnancy complications, namely, serum fluorescence lifetime; the pregnancy complications include preeclampsia, gestational diabetes mellitus, and preeclampsia combined with gestational diabetes mellitus. This invention uses fluorescence lifetime imaging technology to characterize functional changes in the lipid-related microenvironment in maternal serum, enabling the detection of metabolic disturbance signals in the early stages of pregnancy before routine clinical indicators show abnormalities, thereby advancing the disease identification time window. This invention effectively overcomes the problem of difficulty in achieving effective early warning in the subclinical stage of disease in existing technologies, providing a new technical approach for early warning and individualized management of pregnancy complications.

Description

Technical Field

[0001] This invention relates to the field of biological detection technology, specifically to a risk marker for pregnancy complications and its application. Background Technology

[0002] Preeclampsia (PE) and gestational diabetes mellitus (GDM) are among the most common and serious complications of pregnancy, closely associated with adverse outcomes for both mothers and newborns. Epidemiological data shows that the global incidence of preeclampsia is approximately 5%, while the incidence of gestational diabetes mellitus has been steadily increasing over the past two decades. Clinical studies have shown that PE and GDM can co-occur in a certain proportion of pregnant women, leading to a comorbidity of preeclampsia combined with gestational diabetes mellitus (PE + GDM). This comorbidity of PE and GDM is of significant importance in clinical practice. On the one hand, this comorbidity is more complex in its clinical manifestations and pathogenesis, often involving vascular dysfunction, metabolic abnormalities, and endocrine dysregulation. On the other hand, current research lacks consensus on whether GDM exacerbates or alleviates the course of PE. Some studies suggest that GDM may alleviate the severity of PE to some extent, while others indicate that PE + GDM can significantly increase the risk of adverse outcomes such as macrosomia, jaundice, and respiratory complications in newborns. This inconsistency in research findings reflects the clinical and pathological heterogeneity of PE + GDM, suggesting that it may not be a simple superposition of two diseases, but rather a mixed phenotype with relatively independent pathological features.

[0003] Both PE and GDM present with metabolic disturbances in early pregnancy, with abnormalities in lipid metabolism pathways being particularly prominent. Studies have shown that oxidative stress imbalance, vascular or endothelial dysfunction, and insulin resistance work synergistically to lead to lipid metabolism disorders and mitochondrial homeostasis dysregulation, thus causing PE or GDM. These pathological processes are usually accompanied by elevated serum free fatty acid levels, enhanced phospholipid oxidation, and alterations in lipoprotein composition. While the two diseases share commonalities in metabolic abnormalities, their metabolic characteristics differ: PE is mainly characterized by oxidation-related lipid remodeling and abnormal anti-angiogenic responses, while GDM is primarily associated with glucose metabolism disorders, abnormal insulin signaling pathways, and mitochondrial dysfunction. Furthermore, research suggests that PE + GDM may be an independent pathological subtype. Lipidomics analysis revealed that compared to PE or GDM alone, pregnant women with PE + GDM exhibit abnormally elevated levels of lipid molecules such as triglycerides and phosphatidylcholine, presenting overlapping yet differentiated lipid metabolism characteristics. Moreover, these metabolic abnormalities do not only appear after clinical diagnosis but also occur in early pregnancy. Studies have found that significant changes in triglyceride and fatty acid metabolism occur in pregnant women at least ten weeks before a clinical diagnosis of GDM. In pregnant women subsequently diagnosed with PE, disturbances in related metabolic pathways, including those involving amino acids, fatty acids, carbohydrates, and vitamin A, can be observed in early pregnancy.

[0004] Current technologies for predicting and diagnosing PE and GDM primarily rely on serum biomarkers and routine biochemical indicators. For example, placental growth factor (PlGF), soluble tyrosine kinase-1 (sFlt-1), and their ratios are widely used in the clinical assessment of PE, but their diagnostic value is mainly reflected in the mid-to-late stages of pregnancy (approximately 20-34 weeks). In current clinical practice, GDM is usually diagnosed based on an oral glucose tolerance test (OGTT) performed at 24-28 weeks of gestation. Although studies have suggested that circulating molecules such as adiponectin and sex hormone-binding globulin can be used for risk assessment or early prediction, limitations in sensitivity, diagnostic specificity, and population applicability have prevented the establishment of unified and stable clinical diagnostic criteria. This makes it difficult to effectively identify the disease in the subclinical stage, resulting in a delayed diagnostic window and missed opportunities for optimal intervention. Furthermore, current detection strategies are limited by the high complexity of serum samples and the overlap in biochemical characteristics among different pregnancy complications. Their ability to identify subclinical states and PE + GDM comorbid phenotypes in early pregnancy remains insufficient, limiting early intervention and precise risk stratification in clinical practice.

[0005] In summary, given the limitations in identifying metabolic abnormalities in early pregnancy, the insensitivity to changes in the microenvironment, and the limited ability to differentiate comorbid phenotypes, there is an urgent need for a detection method that can functionally reflect changes in the metabolic microenvironment while also considering early detection, sensitivity, and clinical feasibility. The aim is to accurately differentiate between PE, GDM, and PE + GDM in clinical practice, enabling refined phenotyping and risk assessment of complex pregnancy complications, and meeting the need for accurate early clinical warning. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a risk marker for pregnancy complications and its application. It offers excellent timeliness, sensitivity, and stability in the diagnosis of pregnancy complications in the early or mid-pregnancy stages, making it suitable for widespread clinical application.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a risk marker for pregnancy complications, wherein the marker is the fluorescence lifetime of serum; the pregnancy complications include preeclampsia, gestational diabetes mellitus, and preeclampsia combined with gestational diabetes mellitus.

[0008] As a preferred embodiment of the risk marker for pregnancy complications described in this invention, the fluorescence lifetime includes the average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitude of the corresponding lifetime component.

[0009] Secondly, the present invention provides a pregnancy complication risk assessment model, wherein the model uses serum fluorescence lifetime parameter as input variable and pregnancy complication risk as output variable. The serum fluorescence lifetime parameter includes the average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitudes of the corresponding lifetime components; The pregnancy complications include preeclampsia, gestational diabetes, and preeclampsia combined with gestational diabetes.

[0010] Thirdly, the present invention provides a pregnancy complication risk assessment system, the system comprising a detection unit and a data analysis unit, wherein: The detection unit is used to detect serum fluorescence lifetime parameters in serum samples; The data analysis unit is used to analyze and process the detection results of the detection unit shown. The serum fluorescence lifetime parameter includes the average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitudes of the corresponding lifetime components; The pregnancy complications include preeclampsia, gestational diabetes, and preeclampsia combined with gestational diabetes.

[0011] Preferably, the detection unit includes a reagent for detecting serum fluorescence lifetime parameters in the sample.

[0012] Fourthly, the present invention provides a diagnostic reagent for assessing the risk of pregnancy complications, comprising a formulation for detecting serum fluorescence lifetime parameters of serum samples using fluorescence lifetime imaging technology; the serum fluorescence lifetime parameters include average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitude of the corresponding lifetime component.

[0013] Fifthly, the present invention provides a method for assessing the risk of pregnancy complications, comprising the following steps: (1) Detect the serum fluorescence lifetime parameters of serum samples using fluorescence lifetime imaging technology; The detection uses Nile blue as a polar-sensitive fluorescent probe; The serum fluorescence lifetime parameter includes the average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitudes of the corresponding lifetime components; (2) Compare the serum fluorescence lifetime parameters with the corresponding serum fluorescence lifetime parameters of normal pregnancy; and determine the risk level of pregnancy complications; The pregnancy complications include preeclampsia, gestational diabetes, and preeclampsia combined with gestational diabetes.

[0014] In a sixth aspect, the present invention applies the aforementioned risk markers to the prediction of the risk of pregnancy complications in the first or second trimester.

[0015] In a seventh aspect, the present invention applies the pregnancy complication risk assessment model or the pregnancy complication risk assessment system to the prediction of pregnancy complication risks in the early or mid-pregnancy period.

[0016] Eighthly, the present invention relates to the application of the described detection reagent or method in the prediction of the risk of pregnancy complications in the early or mid-stages of pregnancy.

[0017] In a preferred embodiment of the application described in this invention, the early pregnancy period is 8-12 weeks of gestation, and the mid-pregnancy period is 24-28 weeks of gestation.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention uses fluorescence lifetime imaging microscopy (FLIM) to characterize functional changes in the lipid-related microenvironment in maternal serum, enabling the detection of metabolic disturbance signals in the early stages of pregnancy before routine clinical indicators show abnormalities, thereby advancing the disease identification time window.

[0019] This invention incorporates fluorescence lifetime parameters (such as average lifetime). and The ratio is highly sensitive to changes in microenvironment polarity, molecular interactions, and metabolic state, making it more effective at capturing early, subtle, and biologically significant metabolic abnormalities compared to traditional fluorescence intensity or spectral characteristics. Especially during the 8-12 week stage of pregnancy, it can distinguish between normal pregnancy, PE, GDM, and PE + GDM, while conventional biochemical assays and Raman spectroscopy at the same time are still difficult to achieve effective separation, indicating that this invention has a significant advantage in early detection.

[0020] Furthermore, the fluorescence lifetime parameters used in this invention are independent of fluorescence intensity and dye concentration, and are less affected by factors such as photobleaching and excitation power fluctuations, resulting in higher detection stability and repeatability. FLIM results are presented intuitively as pseudo-color lifetime maps and lifetime distributions, directly reflecting changes in the pathological state of the serum microenvironment and improving the interpretability of the results. In practical applications, this invention requires only a small amount of peripheral blood serum sample, the sample preparation process is simple, the detection method is minimally invasive or non-invasive, and it is highly compatible with routine prenatal blood collection procedures, demonstrating good clinical feasibility and potential for widespread application.

[0021] In summary, this invention provides a functional detection method based on fluorescence lifetime, which can effectively differentiate and assess the risks of normal pregnancy, PE, GDM, and PE + GDM in early pregnancy. This overcomes the shortcomings of existing technologies in terms of diagnostic timeliness, sensitivity, and identification of complex phenotypes, and addresses the problem of difficulty in achieving effective early warning in the subclinical stage of disease. It provides a new technical approach for early warning and individualized management of pregnancy complications, and offers a new technical path for early identification, sensitive detection, and risk stratification of pregnancy complications. Attached Figure Description

[0022] Figure 1 Raman spectroscopy reveals the molecular diagnostic features of pregnancy complications; Figure: (a) mean Raman spectra of serum from pregnant women in the control group, PE group, GDM group, and PE + GDM group; (b) principal component analysis (PCA) plot of Raman spectra; (c) sparse partial least squares discriminant analysis (sPLS-DA) plot; (d) heatmap of centroid distance for each group calculated based on multivariate analysis of variance (PERMANOVA).

[0023] Figure 2 The results of lipidomics analysis of serum samples from pregnant women are shown in the figure. (a) Stacked bar chart of relative abundance of various lipids in serum samples from the control group, PE group, GDM group, and PE + GDM group; (b) Distribution of major lipid metabolite categories in different groups; (c) Upset plot of lipids with significant differences in different groups; (d) Bubble plot of lipid metabolites with significant differences in different groups; (e) Heatmap of the top 10 lipid metabolites by abundance.

[0024] Figure 3 The results of FLIM in differentiating fluorescence lifetime changes in different disease states; in the figure: (a) mean fluorescence lifetime of the control group, PE group, GDM group and PE + GDM group ( ), (a) Violin plot of the ratios and a1 / a2 ratios, where the thick dashed line represents the median and the thin dashed line represents the interquartile range; (b) Normalized mean fluorescence lifetime ( (c) Density curve of the distribution; , , (d) Representative fluorescence lifetime imaging images of the sample, with the bottom row showing lifetime images displayed in pseudocolor according to fluorescence lifetime and the top row showing the corresponding fluorescence intensity images; (e)-(g) are the average fluorescence lifetime (and weighted phasor points phasor diagrams); ), ROC curves of the ratio and the a1 / a2 ratio.

[0025] Figure 4 The results differentiate between different disease states in early pregnancy; in the figure: (a) Principal component analysis (PCA) results of Raman spectroscopy; (b) Mean fluorescence lifetime of the control group, PE group, GDM group, and PE + GDM group. ), A violin plot of the ratios and the a1 / a2 ratio, where the thick dashed line represents the median and the thin dashed line represents the interquartile range; (c) Each group , (d) Representative fluorescence lifetime imaging images of the sample, with the bottom row showing lifetime images displayed in pseudocolor based on fluorescence lifetime and the top row showing the corresponding fluorescence intensity images; (e)-(g) Based on average fluorescence lifetime ( ), ROC curves of the ratio and the a1 / a2 ratio.

[0026] Figure 5 Threshold distribution characteristics for different disease states in late pregnancy (a) and early pregnancy (b).

[0027] Figure 6 Validation sets for different disease states Characteristics of changes in a1 / a2. Detailed Implementation

[0028] To better illustrate the objectives, technical solutions, and advantages of this invention, the invention will be further described below with reference to specific embodiments. Those skilled in the art should understand that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0029] Unless otherwise specified, the experimental methods used in the examples are conventional methods; the materials and reagents used are commercially available unless otherwise specified. Example 1:

[0030] (1) Sample collection and grouping Peripheral blood samples were collected from pregnant women and centrifuged to obtain serum. The serum samples could be from pregnant women in early pregnancy (8-12 weeks) (n=108) or mid-pregnancy (24-28 weeks) (n=113). Based on clinical diagnosis or subsequent follow-up outcomes, the samples were categorized into normal pregnancy, preeclampsia, gestational diabetes mellitus, or preeclampsia combined with gestational diabetes mellitus. Each testing group included at least multiple independent serum samples to ensure the stability and representativeness of the test results.

[0031] (2) Raman spectroscopy Raman spectra were acquired using a confocal Raman microscopy system (Renishaw, UK; inVia Raman microscope; WiRE 4.3 software) equipped with a 632.8 nm helium-neon laser excitation source. A 50x objective lens was used to focus the laser onto the sample surface, ensuring the laser power on the sample surface remained below 10 mW to avoid optical damage. The spectral acquisition range was [missing information]. Spectral resolution is Each spectrum contains 3067 data points, with a single acquisition time of 10 seconds. Measurements at each point are accumulated three times to improve the signal-to-noise ratio. Spectra are acquired from three randomly selected locations for each sample to eliminate the influence of spatial heterogeneity. The average spectrum is then used for subsequent analysis. The raw spectra are preprocessed using WiRE software with vector normalization, then imported into R software (version 4.5.0) for dimensionality reduction and supervised classification analysis using the mixOmics package. Specifically, Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) methods are employed. PCA is used to visualize the separation trend between groups, while OPLS-DA and sPLS-DA achieve supervised classification and identify discriminative spectral features.

[0032] (3) Lipidomics analysis Serum samples used for ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) analysis were collected from 94 pregnant women between 24 and 28 weeks of gestation. Lipid extracts were analyzed using a Waters UPLC I-Class Plus system (Waters, Inc.) coupled with a QExactive high-resolution mass spectrometer (Thermo Fisher Scientific, Inc.), with data acquisition covering both positive and negative ion modes.

[0033] The serum sample preparation process is as follows: 100 µL of serum was placed in an EP tube, 300 µL of pre-cooled isopropanol was added, and the tube was vortexed vigorously for 1 minute. Subsequently, the tube was centrifuged at 25,000 × g for 15 minutes at 4°C, and 10 µL of the supernatant was transferred to a new test tube and mixed with the quality control solution.

[0034] Lipidomics analysis was performed on a Waters UPLC I-Class Plus system (Waters, USA) coupled with a QExactive Orbitrap mass spectrometer (Thermo Fisher Scientific, USA). 5 µL of protein-free sample was injected into a CSHC18 column (2.1 × 100 mm, 1.7 µm particle size; Waters, USA). The mobile phase in both positive and negative electrospray ionization modes contained a mixture of acetonitrile, isopropanol, and ammonium formate (partially containing 0.1% formic acid), with the following specific composition: Positive ion mode: Mobile phase A: 60% acetonitrile, containing 10 mM ammonium formate and 0.1% formic acid; Mobile phase B: 90% isopropanol, 10% acetonitrile, containing 10 mM ammonium formate and 0.1% formic acid; Negative ion mode: Mobile phase A: 60% acetonitrile, containing 10 mM ammonium formate; Mobile phase B: 90% isopropanol, 10% acetonitrile, containing 10 mM ammonium formate; Chromatographic separation conditions: column temperature 55℃, flow rate 0.4 mL / min. The gradient elution program was as follows (mobile phase B proportion): 0–2 min, 40–43%; 2–2.1 min, 43–50%; 2.1–7 min, 50–54%; 7–7.1 min, 54–70%; 7.1–13 min, 70–99%; 13–13.1 min, 99–40%; 13.1–15 min, 40%.

[0035] Mass spectrometry data acquisition was performed using an ESI ion source with a scanning range of m / z 200-2000. The parameters were set as follows: sheath gas flow rate 40 L / h; auxiliary gas flow rate 10 L / h; spray voltage +3.8 kV (positive ion mode) / -3.2 kV (negative ion mode); capillary temperature 320℃; auxiliary gas heater temperature 350℃.

[0036] (4) Fluorescent labeling and preparation of serum samples The obtained serum samples were mixed with Nile Blue, a polar-sensitive fluorescent dye. The serum and fluorescent dye were mixed at a volume ratio of 9:1 and thoroughly mixed in a sealed container. Subsequently, the mixed serum-dye solution was uniformly coated onto the surface of a glass slide at a density of [missing information]. This process allows the sample to form a uniform thin layer on the glass slide. After the sample is allowed to dry naturally, it is mounted with neutral resin to maintain the structural integrity and stability of the sample during subsequent imaging.

[0037] (5) Fluorescence lifetime imaging data acquisition Fluorescence lifetime imaging microscopy (FLIM) was used to measure fluorescence decay characteristics, specifically by performing fluorescence lifetime imaging on prepared serum smears. The imaging process employed a fluorescence lifetime imaging system based on the time-correlated single-photon counting (TCSPC) principle, integrated with a confocal microscopy platform. The excitation source was a pulsed laser; for Nile blue-stained samples, the excitation wavelength was 530 nm, coupled with a 550 nm long-pass filter to ensure that only fluorescence emission signals were collected. The excitation light was focused onto the sample surface via a dichroic mirror, scanning system, and objective lens. The fluorescence signal was collected by the same objective lens and transmitted back through the optical path to the detector for detection.

[0038] Each serum smear is preferably imaged in multiple random fields of view, with 10 fields of view acquired for each sample, in order to reduce the impact of spatial heterogeneity on the results.

[0039] (6) Fluorescence lifetime fitting and parameter extraction The collected fluorescence lifetime data were fitted and analyzed using a double exponential decay model, the mathematical expression of which is: ; In the formula, I(t) represents the fluorescence intensity at time t after the excitation pulse; and These represent short lifespan and long lifespan, respectively. and These represent the amplitude of the corresponding lifetime component.

[0040] Average fluorescence lifetime parameter The mathematical expression is: ; In the formula, and These represent short lifespan and long lifespan, respectively. and These represent the amplitude of the corresponding lifetime component.

[0041] (7) Data analysis and judgment The fluorescence lifetime parameters extracted from the ROI region were statistically analyzed. These lifetime parameters were then exported to the data analysis software R (version 4.5.0) for further processing to perform receiver operating characteristic (ROC) curve analysis. By comparing the fluorescence lifetime characteristics of serum samples from different pregnant women, the identification and differentiation of normal pregnancy, PE, GDM, and PE + GDM can be achieved.

[0042] (8) Experimental results 1) Metabolic phenotypic analysis of peripheral blood serum samples from pregnant women was performed using Raman spectroscopy to explore biochemical differences under different pregnancy stages. Raman spectroscopy analysis results. Figure 1 The results showed that there were differences among the control group, PE group, GDM group, and PE + GDM group in several key biochemical vibrational regions, mainly including the lipid-related CH stretching vibration region ( ), carbohydrate and phosphate backbone related regions ( ) and the Amide I protein band (approximately Among the groups, the PE group showed the highest overall spectral intensity, indicating more significant biochemical metabolic abnormalities in its serum, followed by GDM. The spectral characteristics of the PE + GDM group were relatively similar to those of the control group. Principal component analysis (PCA) revealed that although some principal components could distinguish PE from other groups to some extent, there was still significant overlap between PE + GDM and the control group, suggesting that relying solely on overall spectral differences is insufficient for reliable differentiation of mixed phenotypes. Further analysis using sparse partial least squares discriminant analysis (sPLS-DA) and multivariate analysis of variance (PERMANOVA) showed that while the PE group could be relatively clearly separated from other groups, the PE + GDM samples still exhibited partial overlap with normal pregnancy and GDM. Multiple statistical analyses consistently indicated that PE had the most significant biochemical changes, while PE + GDM showed mixed characteristics. Ranking analysis of Raman characteristic peaks showed that the differences between groups were mainly enriched in lipid-related vibrational patterns, supporting the view that lipid metabolism abnormalities are an important early feature of PE and GDM. However, due to the high complexity of serum samples themselves and the shared biochemical characteristics among different pregnancy complications, Raman spectroscopy has limited sensitivity in distinguishing the mixed phenotype of PE + GDM, and it is difficult to fully reflect subtle changes in functional aspects such as lipid remodeling, oxidation state, and molecular microenvironment.

[0043] 2) To elucidate the metabolic differences under different pregnancy complication states and to provide biochemical evidence for the interpretation of the diagnostic method of this invention, lipidomics analysis was performed on all groups. The results are as follows: Figure 2As shown, at the lipid category level, glycerophospholipids and triglycerides consistently accounted for the majority, and the overall lipid abundance in the PE group and the PE + GDM group was significantly higher than that in the control group, suggesting a more pronounced lipid metabolism remodeling under the aforementioned pathological conditions. Among them, phosphatidylcholine (PC) and phosphatidylethanolamine accounted for the largest proportion, followed by sphingolipids and neutral triglycerides, and showed a relative enrichment trend in PE and PE + GDM, further indicating that membrane-associated lipid perturbation is one of the important metabolic characteristics of this type of disease.

[0044] Significant differences in lipid alterations were observed across different disease states, with the largest number of differentially expressed lipids between GDM and PE, followed by the control group and the PE group. Further subclass visualization analysis revealed specific regulatory patterns in different lipid subclasses across each disease state: triglycerides were upregulated in PE, while multiple proteolytic lipids (PCs) and phosphatidylethanolamine molecules were higher in both the GDM and PE groups than in the control group. These results indicate that different pregnancy complication states do not occur through a unified lipid metabolism pathway, but rather involve differential regulation of multiple metabolic pathways.

[0045] Hierarchical clustering analysis of the top 10 most abundant lipid metabolites revealed that the PE samples formed an independent branch, characterized by significantly elevated levels of multiple triglyceride molecules, while the GDM samples were closer to the control group, mainly showing enrichment of some PC molecules. The PE + GDM samples did not present a simple superposition of PE and GDM, but rather a mixed characteristic of simultaneous elevations in triglycerides and PC molecules, reflecting a mixed and nonlinear metabolic state. This result supports considering PE + GDM as a comorbid state with independent metabolic phenotypes, rather than a simple coexistence of two diseases.

[0046] The combined lipidomics results confirm that glycerophospholipid and triglyceride metabolism constitute the core axis of lipid metabolism disorders in PE, GDM, and their comorbidities, with PE exhibiting the most significant lipid remodeling characteristics. Various polyunsaturated triglyceride molecules enriched in PE have been shown to be closely related to vascular dysfunction, oxidative stress, and endothelial damage, while the predominantly PC-like lipid abnormalities in GDM are associated with insulin resistance and abnormal lipid transport. The lipid profiles of PE + GDM show a partially overlapping pattern, further explaining the difficulty in achieving clear separation at the spectral level.

[0047] 3) To assess changes in the serum lipid-related microenvironment, fluorescence lifetime imaging microscopy (FLIM) was employed, and Nile Blue was introduced as a polar-sensitive fluorescent probe. Nile Blue was used to indicate lipid-rich areas, and its fluorescence lifetime is highly sensitive to the surrounding chemical microenvironment, thereby enabling highly sensitive monitoring of lipid-related metabolic microenvironment remodeling processes.

[0048] The results of FLIM differentiation of fluorescence lifetime changes in different disease states in samples from 24-28 weeks of gestation are as follows: Figure 3 As shown in Table 1: Table 1. Statistical differences between groups at 24-28 weeks of gestation FLIM analysis showed that the control group, PE group, GDM group, and PE+GDM group exhibited clear disease-specific differences in fluorescence lifetime parameters (see [link to FLIM analysis]). Figure 3 (ad, Table 1). The mean fluorescence lifetime parameters for the control group are shown below. The value was the highest in the GDM group; compared with the control group, the GDM group had the highest value. The value decreased significantly ( Figure 3 b). It is worth noting that the PE+GDM group The distribution was between the PE and GDM groups, suggesting that their lipid-related metabolic microenvironment represents a mixed metabolic phenotype between two single disease states. Further phase mapping analysis revealed significant changes in fluorescence lifetime decay components across different disease states. The ratio, used to reflect the relative balance between short lifespan and long lifespan decline, was lowest in the control group but significantly higher in all disease groups. Specifically, the GDM group... The increase was most significant in the PE group and the PE+GDM group, indicating that in the GDM state, the fluorescence signal shifted more significantly towards the rapidly decaying component, which was characterized by the short-lived component. The proportion of ) increased and long-life components ( )reduce. The ratio (characterizing the amplitude contribution of rapidly decaying fluorescent components to slowly decaying fluorescent components) also showed a gradual increasing trend from the control group to the PE, GDM, and PE+GDM groups, with the highest value reached in the comorbidity group (PE+GDM). This amplitude change indicates that, under pathological conditions, slowly decaying fluorescent components are gradually replaced by rapidly decaying fluorescent components in the lipid-related microenvironment. FLIM imaging results ( Figure 3 d) The above findings were further validated at the spatial distribution level: the control group showed a uniform and relatively long fluorescence lifetime region; the GDM samples showed a relatively uniform but slightly shorter overall fluorescence lifetime distribution; while the PE group and the PE+GDM group showed significant spatial heterogeneity and an overall shortened fluorescence lifetime signal. To evaluate the application potential of the above fluorescence lifetime parameters in disease diagnosis, this invention further... , and Receiver operating characteristic (ROC) curve analysis was performed (see [link]). Figure 3 e.g.), the results show that and It exhibits high classification performance in distinguishing between disease groups and control groups, demonstrating good diagnostic application value.

[0049] To further evaluate the applicability of the method of the present invention in the early diagnosis of pregnancy-related diseases, serum samples collected at 8-12 weeks of gestation were further selected and tested and analyzed in four groups: normal pregnancy, PE, GDM, and PE+GDM.

[0050] The results are as follows Figure 4 As shown in Tables 2 and 3: Table 2. Characteristics of routine clinical biochemical indicators during weeks 8-12 of gestation Table 3. Statistical differences between groups during weeks 8-12 of gestation. The results showed that routine clinical biochemical indicators and Raman spectroscopy analysis did not show significant differences among the four groups of samples (see Table 2 and...). Figure 4 a) However, based on the ability of FLIM to identify clear group-specific differences (see Table 3), it demonstrates high sensitivity in distinguishing PE, GDM, and PE+GDM in early pregnancy. FLIM analysis results indicate that lipid-related microenvironmental changes have already occurred in serum samples during early pregnancy, resulting in disease-specific fluorescence lifetime patterns. Mean fluorescence lifetime parameters There were clear differences between the different groups. Figure 4 b). Compared to the control group, the GDM group The overall distribution shifted to the right, while the PE group and the PE+GDM group shifted to the left, suggesting that abnormal disturbances in the serum microenvironment occurred in early pregnancy. Further phase mapping analysis revealed that the GDM group... and Both were higher than the control group, indicating that its lipid-related microenvironment tends to be composed of longer-lived components; conversely, the PE group... and All decreased. PE+GDM group It falls between the control group and the PE group, while The results were largely consistent with the control group, suggesting that the long-lived fluorescent components were retained to some extent. Figure 4 c). The highest level was observed in the GDM group, followed by the control group, the PE+GDM group, and the PE group, indicating that... It can serve as a highly sensitive discriminant parameter for different pathological states in early pregnancy, effectively distinguishing between four states: normal pregnancy, PE, GDM, and PE+GDM. The ratios (reflecting the amplitude contributions of short-lived and long-lived fluorescent components) exhibited different patterns of variation. (PE group) The lowest values ​​indicate that the rapidly decaying fluorescent components are suppressed; GDM group and PE+GDM group The value was close to that of the control group, but slightly higher. Figure 4 b). The above differences are visually verified in the FLIM imaging results ( Figure 4 d): GDM samples showed a widely distributed long fluorescence lifetime signal, while the PE group and PE+GDM group showed more obvious spatial heterogeneity and shortened fluorescence lifetime characteristics.

[0051] To further evaluate the diagnostic performance of various fluorescence lifetime parameters in early disease identification, based on , and The parameters were analyzed using ROC (Reference Occurrence Conversion) analysis. Figure 4 e.g.), The results show that, and The highest AUC indicates that the above fluorescence lifetime characteristics can reveal quantifiable microenvironmental differences before the appearance of clinical symptoms, and have strong group differentiation ability and potential for early diagnostic applications.

[0052] Threshold distribution characteristics of different disease states in late and early pregnancy, such as Figure 5 As shown. Example 2:

[0053] Peripheral blood samples were collected from pregnant women at 24-28 weeks of gestation, and serum was obtained by centrifugation. Based on clinical diagnosis or subsequent follow-up outcomes, samples were categorized into normal pregnancy (n=30), preeclampsia (n=30), gestational diabetes mellitus (n=30), or preeclampsia combined with gestational diabetes mellitus (n=30). Each testing group included three independent serum samples to ensure the stability and representativeness of the test results. The obtained serum samples were mixed with Nile Blue, a polar-sensitive fluorescent dye. The serum and fluorescent dye were mixed at a volume ratio of 9:1 and thoroughly mixed in a sealed container. Subsequently, the mixed serum-dye solution was uniformly coated onto the surface of a glass slide at a density of [missing information]. This process allows the sample to form a uniform thin layer on the glass slide. After the sample is allowed to dry naturally, it is mounted with neutral resin to maintain the structural integrity and stability of the sample during subsequent imaging.

[0054] (1) Fluorescence lifetime imaging data acquisition Fluorescence lifetime imaging microscopy (FLIM) was used to measure fluorescence decay characteristics, specifically by performing fluorescence lifetime imaging on prepared serum smears. The imaging process employed a fluorescence lifetime imaging system based on the time-correlated single-photon counting (TCSPC) principle, integrated with a confocal microscopy platform. The excitation source was a pulsed laser; for Nile blue-stained samples, the excitation wavelength was 530 nm, coupled with a 550 nm long-pass filter to ensure that only fluorescence emission signals were collected. The excitation light was focused onto the sample surface via a dichroic mirror, scanning system, and objective lens. The fluorescence signal was collected by the same objective lens and transmitted back through the optical path to the detector for detection.

[0055] Each serum smear is preferably imaged in multiple random fields of view, with 10 fields of view acquired for each sample, in order to reduce the impact of spatial heterogeneity on the results.

[0056] (2) Fluorescence lifetime fitting and parameter extraction The collected fluorescence lifetime data were fitted and analyzed using a double exponential decay model, the mathematical expression of which is: ; In the formula, I(t) represents the fluorescence intensity at time t after the excitation pulse; and These represent short lifespan and long lifespan, respectively. and These represent the amplitude of the corresponding lifetime component.

[0057] Average fluorescence lifetime parameter The mathematical expression is: ; In the formula, and These represent short lifespan and long lifespan, respectively. and These represent the amplitude of the corresponding lifetime component.

[0058] Fluorescence lifetime imaging was used to detect changes in fluorescence lifetime in the peripheral blood samples of the aforementioned pregnant women. Figure 6 As shown, the trend is consistent with that of Example 1. This invention uses fluorescence lifetime imaging as the core detection method. By measuring the fluorescence decay kinetics, it analyzes the functional changes in the lipid-related microenvironment, including oxidative stress, protein-lipid interactions, microenvironment polarity, and metabolic activity changes, thereby achieving higher sensitivity in distinguishing between normal pregnancy, PE, GDM, and their comorbidities.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A risk marker for pregnancy complications, characterized in that, The biomarker is serum fluorescence lifetime; the pregnancy complications include preeclampsia, gestational diabetes mellitus, and preeclampsia combined with gestational diabetes mellitus.

2. The risk markers for pregnancy complications according to claim 1, characterized in that, The fluorescence lifetime includes the average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitude of the corresponding lifetime component.

3. A risk assessment model for pregnancy complications, characterized in that, The model uses serum fluorescence lifetime parameter as input variable and the risk of pregnancy complications as output variable; The serum fluorescence lifetime parameter includes the average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitudes of the corresponding lifetime components; The pregnancy complications include preeclampsia, gestational diabetes, and preeclampsia combined with gestational diabetes.

4. A pregnancy complication risk assessment system, characterized in that, The system includes a detection unit and a data analysis unit, wherein: The detection unit is used to detect serum fluorescence lifetime parameters in serum samples; The data analysis unit is used to analyze and process the detection results of the detection unit shown. The serum fluorescence lifetime parameter includes the average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitudes of the corresponding lifetime components; The pregnancy complications include preeclampsia, gestational diabetes, and preeclampsia combined with gestational diabetes.

5. A diagnostic reagent for assessing the risk of pregnancy complications, characterized in that, This includes formulations that use fluorescence lifetime imaging technology to detect serum fluorescence lifetime parameters in serum samples; The detection uses Nile blue as a polar-sensitive fluorescent probe; The serum fluorescence lifetime parameter includes the average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitude of the corresponding lifetime component.

6. A method for assessing the risk of pregnancy complications, characterized in that, Includes the following steps: (1) Detect the serum fluorescence lifetime parameters of serum samples using fluorescence lifetime imaging technology; The serum fluorescence lifetime parameter includes the average fluorescence lifetime. , ratio, At least one of the ratios; The average fluorescence lifetime The calculation formula is: ; The and These represent short lifespan and long lifespan, respectively. and These represent the amplitudes of the corresponding lifetime components; (2) Compare the serum fluorescence lifetime parameters with the corresponding serum fluorescence lifetime parameters of normal pregnancy; and determine the risk level of pregnancy complications; The pregnancy complications include preeclampsia, gestational diabetes, and preeclampsia combined with gestational diabetes.

7. The application of the risk markers described in claim 1 or 2 in predicting the risk of pregnancy complications in the first or second trimester.

8. The application of the pregnancy complication risk assessment model of claim 3 or the pregnancy complication risk assessment system of claim 4 in the prediction of pregnancy complication risks in early or mid-pregnancy.

9. The application of the detection reagent of claim 5 or the method of claim 6 in the prediction of the risk of pregnancy complications in the first or second trimester.

10. The application according to any one of claims 7-9, characterized in that, The first trimester is defined as 8-12 weeks of gestation, and the second trimester as 24-28 weeks.

Images