Effects of Glucagon-Like Peptide-1 Receptor Agonists on Gut Microbiota in Dehydroepiandrosterone-Induced Polycystic Ovary Syndrome Mice: Compared Evaluation of Liraglutide and Semaglutide Intervention.

Purpose: Polycystic ovary syndrome (PCOS) is a frequent cause of infertility in reproductive-age women. Our work aims to evaluate the effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on gut microbiota, with metabolic parameters including body weight and the hormone profile in PCOS. Patients and Methods: Dehydroepiandrosterone (DHEA)-induced PCOS mice were established and then treated with two GLP-1RAs: liraglutide and novel form semaglutide for four weeks. Changes in body weight and metabolic parameters were measured. Fecal samples were collected and analyzed using metagenomic sequencing. Results: Liraglutide and semaglutide modulated both alpha and beta diversity of the gut microbiota in PCOS. Liraglutide increased the Bacillota-to-Bacteroidota ratio through up-regulating the abundance of butyrate-producing members of Bacillota like Lachnospiraceae. Moreover, liraglutide showed the ability to reverse the altered microbial composition and the disrupted microbiota functions caused by PCOS. Semaglutide increased the abundance of Helicobacter in PCOS mice (p < 0.01) which was the only bacteria found negatively correlated with body weight. Moreover, pathways involving porphyrin and flavonoids were increased after semaglutide intervention. Conclusion: Liraglutide and semaglutide improved reproductive and metabolic disorders by modulating the whole structure of gut microbiota in PCOS. The greater efficacy in weight loss compared with liraglutide observed after semaglutide intervention was positively related with Helicobacter. The study may provide new ideas in the treatment and the underlying mechanisms of GLP-1RAs to improve PCOS.

Discrimination of wheat flour grade based on PSO-SVM of hyperspectral technique.

Rapid detection of wheat flour grade played an important role in the food industry. In this work, hyperspectral technology was used to detect five types of wheat flour. An analysis model was established based on the reflectance of samples at 968 ∼ 2576 nm. Moreover, multivariate scattering correction (MSC), standard normalized variate (SNV), and Savitzky-Golay (S-G) convolution smoothing were used for preprocessing, which was employed to reduce the influence of noise in the original spectrum. In order to simplify the model, competing adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE) and the UVE-CARS algorithm were applied to extract feature wavelengths. Both partial least squares discriminant analysis (PLS-DA) model and support vector machine (SVM) model were established according to feature wavelengths. Furthermore, particle swarm optimization (PSO) algorithm was adopted to optimize the search of SVM model parameters, such as the penalty coefficient c and the regularization coefficient g. Experimental results suggested that the non-linear discriminant model for wheat flour grades was better than the linear discriminant model. It was considered that the MSC-UVE-CARS-PSO-SVM model achieved the best forecasting results for wheat flour grade discrimination, with 100% accuracy both in the calibration set and the validation set. It further shows that the classification of wheat flour grade can be effectively realized by using the hyperspectral and SVM discriminant analysis model, which proves the potential of hyperspectral reflectance technology in the qualitative analysis of wheat flour grade.

Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed.

Moisture content (MC) is one of the most important factors for assessment of seed quality. However, the accurate detection of MC in single seed is very difficult. In this study, single maize seed was used as research object. A long-wave near infrared (LWNIR) hyperspectral imaging system was developed for acquiring reflectance images of the embryo and endosperm side of maize seed in the spectral range of 930-2548 nm, and the mixed spectra were extracted from both side of maize seeds. Then, Full-spectrum models were established and compared based on different types of spectra. It showed that models established based on spectra of the embryo side and mixed spectra obtained better performance than the endosperm side. Next, a combination of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) was proposed to select the most effective wavelengths from full-spectrum data. In order to explore the stableness of wavelength selection algorithm, these methods were used for 200 independent experiments based on embryo side and mixed spectra, respectively. Each selection result was used as input of partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) to build calibration models for determining the MC of single maize seed. Results indicated that the CARS-SPA-LS-SVM model established with mixed spectra was optimal for MC prediction in all models by considering the accuracy, stableness and complexity of models. The prediction accuracy of CARS-SPA-LS-SVM model is Rpre = 0.9311 ± 0.0094 and RMSEP = 1.2131 ± 0.0702 in 200 independent assessment. The overall study revealed that the long-wave near infrared hyperspectral imaging can be used to non-invasively and fast measure the MC in single maize seed and a robust and accurate model could be established based on CARS-SPA-LS-SVM method coupled with mixed spectral. These results can provide a useful reference for assessment of other internal quality attributes (such as starch content) of single maize seed.

Nondestructive monitoring storage quality of apples at different temperatures by near-infrared transmittance spectroscopy.

Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near-infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near-infrared transmittance spectra of apples in the wavelength range of 590-1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient (R p) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.

Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy.

Zearalenone is a contaminant in food and feed products which are hazardous to humans and animals. This study explored the feasibility of the Raman rapid screening technique for zearalenone in contaminated maize. For representative Raman spectra acquisition, the ground maize samples were collected by extended sample area to avoid the adverse effect of heterogeneous component. Regression models were built with partial least squares (PLS) and compared with those built with other variable selection algorithms such as synergy interval PLS (siPLS), ant colony optimization PLS (ACO-PLS) and siPLS-ACO. SiPLS-ACO algorithm was superior to others in terms of predictive power performance for zearalenone analysis. The best model based on siPLS-ACO achieved coefficients of correlation (Rp) of 0.9260 and RMSEP of 87.9132 µg/kg in the prediction set, respectively. Raman spectroscopy combined multivariate calibration showed promising results for the rapid screening large numbers of zearalenone maize contaminations in bulk quantities without sample-extraction steps.

Stronger associations of waist circumference and waist-to-height ratio with diabetes than BMI in Chinese adults.

AIMS: To compare the magnitude of associations of the obesity indicators with the risk of prediabetes and diabetes. METHODS: We performed an individually region-, sex-, and age-matched case and control analysis involving 42 918 Chinese adults aged 20-88 years (6876 matched prediabetes and normal glucose regulation [NGR] groups and 2873 matched newly diagnosed diabetes mellitus [NDM] and NGR groups). RESULTS: Compared with their respective NGR controls, the participants with prediabetes or NDM had significantly higher mean levels of obesity indices as follows: waist circumference (cm), 85.3 vs. 81.8 and 87.9 vs. 82.9; waist-to-height ratio (WHtR), 0.531 vs. 0.509 and 0.546 vs. 0.514; and body mass index (BMI) (kg/m2), 25.4 vs. 24.1 and 25.9 vs. 24.2 (all P < 0.001). The odds ratios (95% confidence intervals) of NDM with waist circumference, WHtR, and BMI per standard deviation (SD) increase were 1.88 (1.80-1.97), 1.88 (1.80-1.97), and 1.69 (1.62-1.76) in the total population. CONCLUSIONS: Mean differences in the three obesity indices were around 0.3 SD between matched prediabetes cases and NGR controls, and around 0.5 SD between matched NDM cases and NGR controls. Waist circumference and WHtR were more strongly associated with diabetes than BMI among Chinese adults.

Development and validation of a non-invasive assessment tool for screening prevalent undiagnosed diabetes in middle-aged and elderly Chinese.

To develop a non-invasive assessment tool and compare it to other assessment tools among middle-aged and elderly Shanghainese, 15,309 individuals, who were 45-70 years old, not previously diagnosed with diabetes, and from a cross-sectional survey conducted between April 2013 and August 2014 in Shanghai, were selected into this study. The participants were randomly assigned to either the exploratory group or the validation group. Undiagnosed diabetes was defined according to the American Diabetes Association diagnostic criteria, and score points were generated according to the logistic regression coefficients. Age, family history of diabetes, hypertension, overweight/obesity, and central obesity all contributed to the constructed model, the Shanghai Nicheng diabetes screening score, with the area under the receiver-operating characteristic curve (AUC) being 0.654 (95% CI 0.637-0.670) in the exploratory group and 0.669 (95% CI 0.653-0.686) in the validation group. The score value of 6 was the optimal cut-point with the largest Youden's index. When applied to the validation group, our model had a similar discriminative ability to the New Chinese Diabetes Risk Score (AUC: 0.669 vs. 0.662, p = 0.187), and performed better than other screening scores for Chinese. However, our model was inferior to fasting plasma glucose, 2-hour plasma glucose, and glycosylated hemoglobin in detecting prevalent undiagnosed diabetes (AUC: 0.669 (0.653-0.686) vs. 0.881 (0.868-0.894), 0.934 (0.923-0.944), and 0.834 (0.819-0.848), all p < 0.001). Although non-invasive models, based on demographic and clinical information, are advisable in resource-scarce developing areas, regular blood glucose screening is still necessary among those aged 45 or older.

Abdominal subcutaneous adipose tissue: a favorable adipose depot for diabetes?

BACKGROUND: Previous studies have documented that visceral adipose tissue is positively associated with the risk of diabetes. However, the association of subcutaneous adipose tissue with diabetes risk is still in dispute. We aimed to assess the associations between different adipose distributions and the risk of newly diagnosed diabetes in Chinese adults. METHODS: The Shanghai Nicheng Cohort Study was conducted among Chinese adults aged 45-70 years. The baseline data of 12,137 participants were analyzed. Subcutaneous and visceral fat area (SFA and VFA) were measured by magnetic resonance imaging. Diabetes was newly diagnosed using a 75 g oral glucose tolerance test. RESULTS: The multivariable-adjusted odds ratios (OR) and 95% confidence intervals (CI) of newly diagnosed diabetes per 1-standard deviation increase in SFA and VFA were 1.29 (1.19-1.39) and 1.61 (1.49-1.74) in men, and 1.10 (1.03-1.18) and 1.56 (1.45-1.67) in women, respectively. However, the association between SFA and newly diagnosed diabetes disappeared in men and was reversed in women (OR 0.86 [95% CI, 0.78-0.94]) after additional adjustment for body mass index (BMI) and VFA. The positive association between VFA and newly diagnosed diabetes remained significant in both sexes after further adjustment for BMI and SFA. Areas under the receiver operating characteristic curve of newly diagnosed diabetes predicted by VFA (0.679 [95% CI, 0.659-0.699] for men and 0.707 [95% CI, 0.690-0.723] for women) were significantly larger than by the other adiposity indicators. CONCLUSIONS: SFA was beneficial for lower risk of newly diagnosed diabetes in women but was not associated with newly diagnosed diabetes in men after taking general obesity and visceral obesity into account. VFA, however, was associated with likelihood of newly diagnosed diabetes in both Chinese men and women.

A seven-year study on an integrated hospital-community diabetes management program in Chinese patients with diabetes.

AIMS: To assess whether an integrated hospital-community diabetes management program could improve major cardiovascular risk factor control among patients with diabetes in real-world clinical settings. METHODS: 985 adults with diabetes in the Shanghai Taopu community health service center were enrolled at baseline and 907 subjects completed the follow-up. The follow-up levels of the metabolic profiles were assessed by their averages during the follow up period. RESULTS: After a mean 7-year follow-up period, heamoglobin A1c, systolic and diastolic blood pressure levels decreased by 0.6%, 5.7mmHg, and 1.5mmHg, respectively (all P<0.001). There was a non-significant difference in low-density lipoprotein cholesterol, while high-density lipoprotein cholesterol increased 1.9mg/dL and triglycerides decreased 28.3mg/dL, respectively (all P<0.001). The percentage of patients with diabetes who met any one of three Chinese Diabetes Society goals (heamoglobin A1c <7.0%, blood pressure <140/80mmHg, and low-density lipoprotein cholesterol <100mg/dL) increased from 58.2% to 70.1%. The chronic diabetes complication screening rates (diabetic retinopathy, diabetic neuropathy, diabetic nephropathy) have significantly increased, from almost zero to 12-78%. CONCLUSIONS: This long-term program has increased the proportions of attaining major cardiovascular risk factors control goals and diabetic chronic complication screening rates among patients with diabetes.

Assessment of Polysaccharides from Mycelia of genus Ganoderma by Mid-Infrared and Near-Infrared Spectroscopy.

Ganoderma lingzhi (G. lingzhi), G. sinense, G. applanatum, etc. belongs to the Ganoderma genus of polypore mushrooms which contain rich polysaccharides valuable for nutrition and positive medicinal effects. In order to evaluate polysaccharide content in Ganoderma mycelia obtained in the fermentation process quickly and accurately, in this work we employed infrared spectroscopy to examine different Ganoderma stains of samples from diversified sources. Through mid-infrared (mid-IR) spectroscopy, we could identify the most relevant spectral bands required for polysaccharide evaluation, and through near-infrared (NIR) spectroscopy, we could establish the quantification model for making satisfactory prediction of polysaccharide ingredient content. As such, we have achieved an effective and convenient approach to quantitative assessment of the total polysaccharides in Ganoderma mycelia but also demonstrated that infrared spectroscopy can be a powerful tool for quality control of Ganoderma polysaccharides obtained from industrial production.

Distribution and related factors of cardiometabolic disease stage based on body mass index level in Chinese adults-The National Diabetes and Metabolic Disorders Survey.

BACKGROUND: It is important to characterize distribution of cardiometabolic disease (CMD) based on different body mass index (BMI) levels in a population. This information remains scarce in China, so we investigated the proportions and related factors of cardiometabolic disease stages based on different BMI levels in Chinese adults. METHODS: We included 45 093 participants aged ≥20 years from the National Diabetes and Metabolic Disorders Survey. Cardiometabolic disease (central obesity, elevated triglycerides, elevated blood pressure, elevated plasma glucose, reduced high-density lipoprotein cholesterol, and cardiovascular disease) was classified as stage 0 (no CMD), stage 1 (mild-to-moderate CMD), or stage 2 (severe CMD). Overweight/obesity was defined as BMI ≥25 kg/m2 . RESULTS: The standardized proportions of stage 0, stage 1, and stage 2 were 32.6%, 36.4%, and 30.9% in normal-weight men, 29.9%, 42.5%, and 27.7% in normal-weight women, 4.9%, 31.7%, and 63.4% in overweight/obese men, and 6.9%, 31.4%, and 61.7% in overweight/obese women, respectively. Multinomial regression showed that regardless of gender or region, the probability of severe cardiometabolic disease rapidly increased with increasing BMI. Severe cardiometabolic disease risk was positively associated with ageing, family history of diabetes, hypertension, or cardiovascular disease, but was inversely associated with higher levels of education and increased physical activity. CONCLUSIONS: Of Chinese men and women with normal weight, more than one third had mild-to-moderate cardiometabolic disease, and less than one third had severe cardiometabolic disease, while of these with overweight or obesity, nearly one third had mild-to-moderate cardiometabolic disease, and nearly two thirds had severe cardiometabolic disease.

[Research on fast detecting tomato seedlings nitrogen content based on NIR characteristic spectrum selection].

In order to improve the accuracy and robustness of detecting tomato seedlings nitrogen content based on near-infrared spectroscopy (NIR), 4 kinds of characteristic spectrum selecting methods were studied in the present paper, i. e. competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variables elimination (MCUVE), backward interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS). There were totally 60 tomato seedlings cultivated at 10 different nitrogen-treatment levels (urea concentration from 0 to 120 mg . L-1), with 6 samples at each nitrogen-treatment level. They are in different degrees of over nitrogen, moderate nitrogen, lack of nitrogen and no nitrogen status. Each sample leaves were collected to scan near-infrared spectroscopy from 12 500 to 3 600 cm-1. The quantitative models based on the above 4 methods were established. According to the experimental result, the calibration model based on CARS and MCUVE selecting methods show better performance than those based on BiPLS and SiPLS selecting methods, but their prediction ability is much lower than that of the latter. Among them, the model built by BiPLS has the best prediction performance. The correlation coefficient (r), root mean square error of prediction (RMSEP) and ratio of performance to standard derivate (RPD) is 0. 952 7, 0. 118 3 and 3. 291, respectively. Therefore, NIR technology combined with characteristic spectrum selecting methods can improve the model performance. But the characteristic spectrum selecting methods are not universal. For the built model based or single wavelength variables selection is more sensitive, it is more suitable for the uniform object. While the anti-interference ability of the model built based on wavelength interval selection is much stronger, it is more suitable for the uneven and poor reproducibility object. Therefore, the characteristic spectrum selection will only play a better role in building model, combined with the consideration of sample state and the model indexes.

[Study on quality detection of milk powder based on near infrared spectroscopy (NIR)].

The traditional NIR model was usually built according to various parameters of an individual type of milk powder so that it's really time-consuming. To simplify the application of NIR in real-time quality detection of milk powder, it was proposed in the present paper to build NIR models for a sample set composed of different types of milk powder. With 70 samples provided by one manufacturer, 6 NIR models including acidity, fat, lactose, sucrose, protein and ash, were built by optimizing algorithms. The results indicated that these NIR models except the acidity model have good stability and high prediction ability (RSD<10%, RPD>3).