Differences in in vivo absorption of flavone glycosides, flavone aglycones and terpene lactones under different dosage forms and physiological conditions

ETHNOPHARMACOLOGICAL RELEVANCE

Ginkgo biloba L. extract (GBE) oral preparations have been used for many years in the prevention and treatment of cardiovascular and cerebrovascular diseases, and the main active ingredients are flavonoids and terpene lactones. Among them, the oral absorption of the prototype components of flavonoid glycosides into the blood needs to be further clarified, and the differences in the oral absorption of different components in GBE by different dosage forms and physiological conditions are not clear yet.

AIM OF THE STUDY

To clarify the oral absorption of the prototype flavonoid glycosides in vivo, and to further explore the differences in the oral absorption of various active compounds under different oral dosage forms and dietary conditions.

MATERIALS AND METHODS

Firstly, the target compounds were selected based on the characteristic chromatogram of GBE and literature. Then, the content differences of three different oral GBE preparations were studied, and their pharmacokinetics (PK) were compared. Finally, the PK differences of the preparations with better oral absorption under different dietary conditions were studied.

RESULTS

Five flavonoid glycosides, three aglycones and four terpene lactones were selected as the research objects. The content determination results of GBE tablets, guttate pills and tinctures showed that the content of several components especially flavonoid glycosides in the tincture was higher than that of the other two preparations. After oral administration of these three preparations, the PK study showed different results from previous studies. The PK behavior of flavonoid glycosides was also determined at the same time as flavonoid glycosides and terpene lactones. and the bioavailability of flavonoid glycosides in the tincture was higher than that of the other two preparations. PK results of fasting and non-fasting showed that taking GBE tincture on an empty stomach increased the absorption of various compounds, especially flavonoid glycosides. However, due to the existence of food residues in the gastrointestinal tract, the oral bioavailability of flavonoid glycosides was significantly improved.

CONCLUSIONS

This study discussed the differences in the content and oral absorption of active compounds in different oral preparations of GBE, clarified the in vivo absorption of flavonoid glycosides prototype, as well as the influence of diet on the PK of active compounds, which has certain guiding significance for the clinical application of GBE oral preparations.

Environmental exposure to urinary Bisphenol-A in North Indian children aged between 6 and 16 years and its association with body mass index

This study investigates the effect of environmental risk factors among North Indian children aged between 6 and 16 years associated with body mass index by comparing fasting and non-fasting urine Bisphenol-A, urine creatinine, and serum thyroid levels with other potential confounding factors. A total of 301 children were enrolled, and term fasting and non-fasting were used based on the sample collection, either first morning or random. Children were grouped into obese, non-obese, and underweight categories based on age- and gender-specific Centre for Disease Control (CDC) 2000 growth chart and grouped according to their body mass index. The overall mean ± SD (standard deviation) of age (years) was 10.45±2.39 (boys age 10.38±2.38 and girls age 10.45±2.40). Urinary Bisphenol-A concentration (ng/mL ± SD) was higher among obese (2.40±4.70) than non-obese (1.30±1.67) and underweight (1.84±3.46) category children. Higher levels of fasting urinary Bisphenol-A (≥2 ng/mL) and thyroid-stimulating hormone (TSH) (≥12.7 μg/dL) were associated with obesity than those of non-fasting children. Being non-fasting among girls at quartile 4 (obese, >2.1800 ng/mL; underweight, >2.1325 ng/mL) had higher odds (obese adjusted odds ratio (aOR) 7.72 (95% CI 0.08-1368.52), underweight adjusted odds ratio (aOR) 12.45 (95% CI 1.55-171.24)] than fasting children. We recommend awareness programs and policy guidelines by engaging teachers and parents for the physical assessment of the presence of Bisphenol-A in food packaging materials to reduce the potential exposure to other chemical migrations sources. Graphical abstract.

The difference between fasting and non-fasting lipid measurements is not related to statin treatment

Background

Non-fasting blood samples are routinely used to assess plasma lipid profiles except in patients with severe hypertriglyceridemia according to the previous consensus. However, the impact of statin use on non-fasting plasma lipid measurements has not been thoroughly evaluated.

Methods

In this cross-sectional study, 686 patients with normal triglyceride (TG) levels, who were hospitalized due to chest pain, were enrolled. Fasting (8–12 h) and non-fasting (2–4 h after breakfast) lipid profiles were measured on the second day of admission. Patients were divided into the non-statin (n=499) group and statin treatment (n=187) group. Differences in fasting and non-fasting lipid profiles between the statin and non-statin groups were compared.

Results

The mean age of participants was 57±13 years, and 54.4% were male. A linear correlation was observed between fasting and non-fasting lipid profiles. Although a postprandial impact on lipid parameters was observed, the general pattern of differences between fasting and non-fasting lipids was similar in both groups. Additionally, the diff(%) [(non-fasting-fasting)/fasting ×100%] of lipid panels did not vary by statin treatment. Moreover, no effects of statin types or duration of use on non-fasting lipid profiles were identified.

Conclusions

The current study found fasting and non-fasting lipid panels were similar in individuals with or without statin treatment. Non-fasting lipid panels were not significantly affected by statin types or duration of use, suggesting that non-fasting lipid measurement is an acceptable test for patients receiving statin treatment.

Determination of optimal cut-off points after a high-fat meal corresponding to fasting elevations of triglyceride and remnant cholesterol in Chinese subjects

BACKGROUND

Postprandial high triglyceride (HTG), marking elevated level of remnant cholesterol (RC), is an independent risk factor of coronary heart disease (CHD). The postprandial cut-off points for HTG and high RC (HRC) after a daily meal are recommended as 2.0 mmol/L and 0.9 mmol/L, respectively, by the European Atherosclerosis Society (EAS), while those after a high-fat meal in Chinese subjects were not explored.

METHODS

Ninety subjects, including 60 CHD patients (CHD group) and 30 non-CHD controls (CON group), were enrolled in this study. Serum levels of blood lipids, including calculated RC, were monitored at 0, 2, 4 and 6 h after a high-fat meal with 800 kcal and 50 g fat. Analysis of c-statistic was used to determine the cut-off points for postprandial HTG and HRC.

RESULTS

Postprandial levels of triglyceride (TG) and RC significantly increased and peaked at 4 h after a high-fat meal in two groups, although those in CHD group were significantly higher (P < 0.05). The optimal cut-off point to predict HTG at 4 h corresponding to fasting TG ≥ 1.7 mmol/L was 3.12 mmol/L, and that to predict HRC at 4 h corresponding to fasting RC ≥ 0.8 mmol/L was 1.36 mmol/L. According to the new cut-off points, the omissive diagnosis rates of postprandial HTG and HRC decreased obviously.

CONCLUSION

The cut-off points of postprandial HTG and HRC in Chinese subjects after a high-fat meal were higher than those after a daily meal recommended by the EAS, indicating that specific cut-off points should be determined after a certain high-fat meal.

Comparison of remnant cholesterol levels estimated by calculated and measured LDL-C levels in Chinese patients with coronary heart disease

BACKGROUND

Evidence about whether remnant cholesterol (RC), especially non-fasting RC, is a causal risk factor for coronary heart disease (CHD) in Chinese subjects is rare. Recently, estimated RC level (RCe) was applied in many studies with large population. We aimed to compare fasting and non-fasting RCe calculated by LDL-C level determined by different methods in Chinese subjects, and investigate their contributions to CHD.

METHODS

Levels of TC, TG and HDL-C were measured directly in 273 CHD patients (CHD group) and 136 controls (CON group) before and at 4 h after a daily breakfast. LDL-C level was measured directly or calculated by Friedewald equation at TG < 4.5 mmol/L. RC level estimated by calculated or measured LDL-C was termed as RCe1 or RCe2. Contributions of different RC levels to CHD were evaluated by multivariable logistic regression analysis.

RESULTS

Both RCe1 and RCe2 increased significantly at 4 h after breakfast (both p < 0.05). RCe1 was significantly higher than RCe2 in fasting or non-fasting state (p < 0.05). RCe1 was closely related to RCe2, especially in the highest quartile of RCe1 (p < 0.05). Non-fasting RCe1 or RCe2 and fasting RCe2 independently predicted CHD after adjustment for traditional risk factors (all p < 0.05).

CONCLUSIONS

Although RCe1 was significantly higher than RCe2, non-fasting RCe, no matter RCe1 or RCe2, after a daily breakfast was an independent predictor for CHD risk in Chinese subjects, indicating that the non-fasting state is critical in the development of atherosclerosis.

Non-fasting bioelectrical impedance analysis in cystic fibrosis: Implications for clinical practice and research

BACKGROUND

Nutritional status affects pulmonary function in cystic fibrosis (CF) patients and can be monitored by using bioelectrical impedance analysis (BIA). BIA measurements are commonly performed in the fasting state, which is burdensome for patients. We investigated whether fasting is necessary for clinical practice and research.

METHODS

Fat free mass (FFM) and fat mass (FM) were determined in adult CF patients (n = 84) by whole body single frequency BIA (Bodystat 500) in a fasting and non-fasting state. Fasting and non-fasting BIA outcomes were compared with Bland-Altman plots. Pulmonary function was expressed as Forced Expiratory Volume at 1 s percentage predicted (FEV1%pred). Comparability of the associations between fasting and non-fasting body composition measurements with FEV1%pred was assessed by multiple linear regression.

RESULTS

Fasting FFM, its index (FFMI), and phase angle were significantly lower than non-fasting estimates (-0.23 kg, p = 0.006, -0.07 kg/m², p = 0.002, -0.10°, p = 0.000, respectively). Fasting FM and its index (FMI) were significantly higher than non-fasting estimates (0.22 kg, p = 0.008) 0.32%, p = 0.005, and 0.07 kg/m², (p = 0.005). Differences between fasting and non-fasting FFM and FM were <1 kg in 86% of the patients. FFMI percentile estimates remained similar in 83% of the patients when measured after nutritional intake. Fasting and non-fasting FFMI showed similar associations with FEV1%pred (β: 4.3%, 95% CL: 0.98, 7.70 and β: 4.6%, 95% CI: 1.22, 8.00, respectively).

CONCLUSION

Differences between fasting and non-fasting FFM and FM were not clinically relevant, and associations with pulmonary function remained similar. Therefore, BIA measurements can be performed in a non-fasting state.

Comparison of non-fasting LDL-C levels calculated by Friedewald formula with those directly measured in Chinese patients with coronary heart disease after a daily breakfast

BACKGROUND

LDL-C level can be measured by direct methods (LDL-C_M) or calculated by Friedewald formula (LDL-C_C). The aim of this study was to investigate the difference between LDL-C_M and LDL-C_C after a daily breakfast in Chinese patients with coronary heart disease (CHD).

METHODS

Three hundred and three inpatients, including 203 CHD patients (CHD group) and 100 non-CHD controls (CON group), were enrolled in this study. Serum levels of blood lipid parameters, including LDL-C_C and LDL-C_M, at 0, 2 and 4 h (h) were monitored after a daily breakfast in all subjects.

RESULTS

LDL-C_M was significantly higher than LDL-C_C in fasting state in each group and at 4 h postprandially in CHD group (P < .05). Postprandial LDL-C_M and LDL-C_C significantly decreased in each group (P < .05). Postprandial decline in LDL-C_M was significantly greater than that of LDL-C_C (P < .05). For CHD patients taking statins for ≥1 month before admission, non-fasting percent attainment of LDL-C_M or LDL-C_C was significantly higher than its fasting value, especially at 4 h (P < .05). The percent deviation of LDL-C_M from 1.8 mmol/L at 4 h was significantly different from its fasting value. However, there was no significant difference in percent deviation of LDL-C_C from 1.8 mmol/L between fasting and non-fasting states.

CONCLUSIONS

It indicated that the clinical monitoring of non-fasting LDL-C level in CHD patients could be relatively complex, and the judgement may depend not only on the method to acquire LDL-C level, but also on the evaluation method.

Non-fasting lipids detection and their significance in pregnant women

Background

The majority of pregnant women present an increase in lipids. To investigate the influence of the non-fasting state in the lipid and lipoprotein profile in pregnancy, we have aimed to assess the dynamic change of serum lipid and lipoprotein profile with serum glucose in pregnancy to contrast the differences between fasting and non-fasting state.

Methods

Forty-five pregnant women and 41 controls were included in our study. All serum samples were assayed for TC, TG, HDL-C, LDL-C, ApoB, ApoA-1, Lp(a), sdLDL, and Glu concentrations. The comparison between pregnant women and controls (fasting and 2 h after breakfast), differences of these measurement results at three point-in-time, the associations between the concentrations of serum lipid and some maternal and fetus characteristics was conducted with statistical analysis.

Results

Except Glu (p < 0.001), there were no significant differences of all lipids between three point-in-time in pregnant women (p > 0.1). The statistically higher levels were found in fasting TC (p = 0.003), TG (p = 0.019), LDL-C (p = 0.002), ApoB (p = 0.001), ApoA1 (p = 0.013) and sdLDL (p < 0.001) of pregnant women compared with controls. Besides, the statistically significances were also found in 2-h TC (p = 0.001), LDL-C (p = 0.001), ApoB (p < 0.001), Glu (p = 0.013), ApoA-1 (p = 0.009) and sdLDL (p < 0.001) of pregnant women compared with controls. Otherwise, in non-fasting status (2 h after breakfast), pregnancy complication was relevant to TC (p = 0.041), HDL-C (p = 0.014), Glu (p = 0.004). Delivery mode was relevant to TC (p = 0.012), HDL-C (p = 0.013), LDL-C (p = 0.026), ApoA-1 (p = 0.012), and sdLDL (p = 0.044). BMI was relevant to TG (p = 0.027).

Conclusion

We have suggested the non-fasting lipids detection can be used for estimate lipid metabolism in pregnant women.

Changes in non-fasting concentrations of blood lipids after a daily Chinese breakfast in overweight subjects without fasting hypertriglyceridemia

BACKGROUND

Overweight is always accompanied by hypertriglyceridemia (HTG), but the change in non-fasting triglyceride (TG) concentration in overweight subjects without postprandial hypertriglyceridemia was unknown.

METHODS

Concentrations of serum lipids were measured at 2 and 4 h in matched overweight (OW group, n = 54) and control subjects (CON group, n = 55) after a daily meal. Concentrations of remnant cholesterol and non-HDL cholesterol were calculated according to the formulas. The diagnostic criteria for non-fasting HTG were based on 2 different consensus statement. ROC curve was used to determine the pointcut of postprandial HTG.

RESULTS

OW group had higher fasting concentrations of RC and non-HDL-C than CON group. Non-fasting concentrations of triglyceride and RC significantly increased in 2 groups while were higher in OW group (p < .05). The proportion of non-fasting HTG increased after a daily meal in OW group was significantly higher than the percentage of fasting HTG (p < .05). There was a significant correlation between the postprandial concentrations of TG and RC.

CONCLUSIONS

Overweight subjects were more likely to develop non-fasting hypertriglyceridemia and higher concentrations of RC and non-HDL-C. Additionally, 2.0 mmol/l at 4 h after breakfast could be a pointcut value to detect changes in lipid profile of Chinese overweight people.

Hemodilution is not the only reason of difference: Comparison of fasting and non-fasting lipoproteins in paired samples

INTRODUCTION

Low-density lipoprotein cholesterol (LDL) is an important risk factor for cardiovascular disease (CVD) and generally measured after 8-12 h fasting. However, some recent studies have pointed that non-fasting lipoproteins, especially LDL concentrations, are better indicators for demonstrating CVD risk and atherosclerosis. They asserted that nutrition is a negligible factor on changes in lipoprotein concentrations and claimed this difference as a result of hemodilution effect, caused from fluid intake and can be eliminated by applying some adjustments. We aimed to compare the fasting and non-fasting LDL values of the same individuals and discuss whether non-fasting and fasting LDL results can be used in place of each other, directly or after applying hemodilution correction models.

MATERIAL AND METHODS

Fasting and non-fasting blood samples of 248 apparently healthy participants were collected. Lipid panel tests, albumin and hemoglobin levels were studied in each sample. Results were evaluated in seven different models which were recommended to correct the hemodilution effect on fasting and non-fasting lipid concentrations of the same individual. Concordance of fasting and non-fasting risk group of the individual were calculated according to the National Cholesterol Education Program classification.

RESULTS

Fasting and non-fasting LDL and non-high density lipoprotein cholesterol (non-HDL) concentrations were significantly different in every model (p < 0.001). Concordance results of fasting and non-fasting LDL and non-HDL risk groups were 63.8% and 77.9% respectively.

CONCLUSIONS

Our results demonstrated that fasting and non-fasting LDL and non-HDL concentrations could not be used in place of each other even when the results were adjusted for elimination of the hemodilution effect.

Postprandial Lipid Concentrations and Daytime Biological Variation of Lipids in a Healthy Chinese Population

Background

Several latest guidelines and consensus statements from Europe and the United States specify that there is no need for fasting prior to routine lipid tests. However, the latest Chinese guidelines still recommend fasting tests owing to a lack of local evidence. This study aimed to investigate postprandial lipid concentrations and daytime biological variation of lipids in a healthy Chinese population.

Methods

Venous blood samples were collected from 41 ostensibly healthy Chinese volunteers at five time points during the day (06:30, 09:00, 12:00, 15:00, and 18:30). The same batch of reagents was used to determine lipid concentrations. A nested ANOVA was performed to calculate within-subject biological variation (CV_I) and between-subject biological variation (CV_G).

Results

Postprandial concentrations of triglyceride were higher than fasting concentrations, with the maximum change occurring at 12:00 (0.5 hours after lunch, 0.21±0.65 mmol/L difference). The daytime biological variation of triglycerides was relatively high (CV_I=25%, CV_G=35.9%). The postprandial concentrations of total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, apolipoprotein A1, and apolipoprotein B were mostly lower than the fasting concentrations, and their daytime biological variations were relatively low (CV_I=2.4–4.4%, CV_G=11.8–18.7%).

Conclusions

As most daytime lipid concentrations changed only slightly, non-fasting samples could be used for routine lipid tests. However, in cases of abnormal postprandial triglyceride concentrations, dietary factors and fasting time should be considered when interpreting the results.

Risk of misclassification with a non-fasting lipid profile in secondary cardiovascular prevention

AIMS

Routinely fasting is not necessary for measuring the lipid profile according to the latest European consensus. However, LDL-C tends to be lower in the non-fasting state with risk of misclassification. The extent of misclassification in secondary cardiovascular prevention with a non-fasting lipid profile was investigated.

METHODS AND RESULTS

329 patients on lipid lowering therapy for secondary cardiovascular prevention measured a fasting and non-fasting lipid profile. Cut-off values for LDL-C, non-HDL-C and apolipoprotein B were set at <1.8mmol/l, <2.6mmol/l and <0.8g/l, respectively. Study outcomes were net misclassification with non-fasting LDL-C (calculated using the Friedewald formula), direct LDL-C, non-HDL-C and apolipoprotein B. Net misclassification <10% was considered clinically irrelevant. Mean age was 68.3±8.5years and the majority were men (79%). Non-fasting measurements resulted in lower LDL-C (-0.2±0.4mmol/l, P<0.001), direct LDL-C (-0.1±0.2mmol/l, P=0.001), non-HDL-C (-0.1±0.4mmol/l, P=0.004) and apolipoprotein B (-0.02±0.10g/l, P=0.004). 36.0% of the patients reached a fasting LDL-C target of <1.8mmol/l with a significant net misclassification of 10.7% (95% CI 6.4-15.0%) in the non-fasting state. In the non-fasting state net misclassification with direct LDL-C was 5.7% (95% CI 2.1-9.2%), 4.0% (95% CI 1.0-7.4%) with non-HDL-C and 4.1% (95% CI 1.1-9.1%) with apolipoprotein B.

CONCLUSION

Use of non-fasting LDL-C as treatment target in secondary cardiovascular prevention resulted in significant misclassification with subsequent risk of undertreatment, whereas non-fasting direct LDL-C, non-HDL-C and apolipoprotein B are reliable parameters.

Serum Lipid Profile: Fasting or Non-fasting?

Serum lipid profile has now become almost a routine test. It is usually done in fasting state due to certain limitations in non-fasting serum sample. In the recent past efforts have been made to simplify blood sampling by replacing fasting lipid profile with non-fasting lipid profile. However, fasting specimen is preferred if cardiovascular risk assessment is based on total cholesterol, LDL cholesterol or non-HDL cholesterol. A lot has yet to be done in this area. Till then we have to believe in fasting lipid profile for assessment and management of cardiovascular disease.