2023 Volume 70 Issue 5 Pages 529-539
The NOD-like receptor protein 3 (NLRP3) inflammasome plays a key role in lipid metabolism. We used an oral fat tolerance test (OFTT) to detect whether serum NLRP3 levels differed in people with different fat tolerances and evaluate whether NLRP3 was associated with impaired fat tolerance (IFT) and hypertriglyceridemia (HTG). We performed the OFTT using 176 volunteers. The groups were divided according to fasting and postprandial triglyceride (TG) levels: 1) normal fat tolerance (NFT) group (TG at 0 h <1.7 mmol/L and TG at any time point <2.5 mmol/L); 2) IFT group (TG at 0 h <1.7 mmol/L and TG at any time point >2.5 mmol/L); and 3) HTG group (TG at 0 h ≥1.7 mmol/L). With decreased lipid tolerance, the TG and NLRP3 levels increased gradually before a high-fat meal and at any time point after 0 h. NLRP3 levels reached a peak 2 h after meal consumption in all three groups. After adjustment for confounding indicators, logistic regression analysis revealed that fasting serum NLRP3 levels were positively associated with both IFT and HTG (for IFT, odds ratio [OR]: 1.079 [1.037–1.123], p < 0.001; for HTG, OR: 1.085 [1.049–1.123], p < 0.001). According to the receiver operating characteristic curve, fasting serum NLRP3 levels were an effective biomarker for IFT and HTG diagnosis. These results indicate that the fasting serum NLRP3 is an independent risk factor for IFT and HTG, and is a valuable indicator for the early diagnosis of IFT and HTG.
IN CHINA, the prevalence of hypertriglyceridemia (HTG) is gradually increasing owing to rapid social economy development, improvement of the standard of living, and lifestyle changes. Epidemiological surveys have shown that the main causes of dyslipidemia are HTG and low high-density lipoprotein-cholesterolemia in Chinese adults [1], and the prevalence of HTG reached percentages as high as 12.7% [2]. According to previous research, the risk of coronary atherosclerotic heart disease and stroke may be independently predicted by TG [3]. A study regarding the general population of Copenhagen found that elevated postprandial triglyceride (TG) concentrations were correlated with the risks of ischemic stroke, ischemic heart disease, myocardial infarction, and death [4, 5]. Furthermore, postprandial triglyceride levels are more reliable for predicting cardiovascular risk than fasting triglyceride levels [6, 7]. A standardized oral fat tolerance test (OFTT) was recommended in 2019 by Kolovou et al. [8] for detecting postprandial hypertriglyceridemia (PHTG). A postprandial TG concentration >2.5 mmol/L is mandatory during the OFTT. Timely identification of fasting and postprandial HTG is crucial for the early detection of cardiovascular and cerebrovascular diseases. Evaluating the pathogenesis of HTG can provide suggestions for diagnosis and treatment, and for the prevention of lipid metabolism-related diseases.
Studies have shown that, compared with healthy people with normal triglycerides, the serum levels of inflammatory cytokines in people with hypertriglyceridemia are considerably increased [9-12]. The NOD-like receptor protein 3 (NLRP3) inflammasome, as a connection between inflammation and lipid metabolism, is the most widely studied and most representative inflammasome. The inflammasome structure includes NLRP3, the adaptor molecule apoptosis-associated speck-like protein, and the effector molecule, procaspase-1. Activation of the NLRP3 inflammasome promotes the conversion of procaspase-1 into mature caspase-1. Eventually, this in turn leads to the activation and release of interleukin- (IL)-18 and IL-1β [13]. Lipid metabolism disorders are closely related to the NLRP3 inflammasome. The NLRP3 inflammasome can promote lipid deposition in certain tissues and organs [14-18]. The correlation between serum NLRP3 expression and lipid metabolism in people with different lipid tolerances has not yet been elucidated. Therefore, after a high-fat diet test, we tested serum NLRP3 levels in people with different lipid tolerances. We further evaluated whether NLRP3 was associated with impaired fat tolerance (IFT) and HTG, to provide the diagnostic basis for early detection of these conditions.
From May 2018 to December 2019, 176 volunteers were recruited from the endocrinology outpatient department [19]. The Hebei General Hospital Ethics Committee approved this study (2018, No. 2), and the study has been recorded by the China Clinical Trial Registry (No. ChiCTR1800019514). The study complies with the Helsinki’s Declaration guiding principles. Before the study, written informed consent was obtained from all volunteers.
Exclusion criteriaWe excluded volunteers who were vegetarian, pregnant, or who had malignant tumors, familial hypercholesterolemia, heart disease, thyroid dysfunction, diabetes, renal insufficiency, infectious disease, or mental disorder. We further excluded those who used medications that influence inflammation and lipid metabolism (anti-inflammatory medications, contraceptives, fish oil, hormones, and lipid-lowering drugs) and those with stroke, trauma, or a history of surgery and weight change of more than 3 kg within 90 days before the study.
All volunteers underwent an oral glucose tolerance test (OGTT), and patients with diabetes were excluded according to the 1999 WHO diagnostic criteria for diabetes.
Oral fat tolerance testThe study subjects were given a 1-week period of diet washout before the study, during which they were recommended not to eat high-fat or high-protein foods. High-fat meals (1,500 kcal) for the trial were prepared by professional nutritionists, as previously reported [19]. The subjects fasted after 22:00 on the first day of the trial and ate a high-fat meal at 08:00 on the second day. All subjects ate the meal within 10 min, after which eating and drinking (except water) was prohibited for 6 h. Smoking and vigorous activity were not permitted. Venous blood samples were taken before and at 2 h, 4 h, and 6 h after the OFTT meal. Serum was extracted and maintained at –80°C until further analysis.
Measurement of anthropometric, clinical, and biochemical parametersGender, age, height, weight, waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were obtained from all subjects by trained professionals. Body mass index (BMI) = weight (kg)/height (m)2, total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB) were determined before and 2 h, 4 h, and 6 h after the OFTT meal. Fasting blood glucose (FBG), 2-h OGTT postprandial blood glucose, serum uric acid (SUA), and lipid profiles were determined using a Hitachi 7600 automatic biochemical analyzer (Hitachi, Tokyo, Japan). Non-HDL-C and TG-rich lipoprotein remnants (TRLRs) were calculated by the following formulas: non-HDL-C = TC-HDL-C; TRLRs = TC – (HDL-C) – (LDL-C). Fasting serum insulin (FINS) and 2-h OGTT postprandial insulin were detected by electrochemiluminescence. A homeostasis model (HOMA-IR) (FBG [mmol/L] × FINS [μIU/mL]/22.5) and the β-cell function index (HOMA-β) (20 × FINS [μIU/mL]/FBG [mmol/L] – 3.5) were used to assess insulin resistance. Serum NLRP3 levels were measured using the NLRP3 ELISA kit (CSB-E15885h; Cusabio, China), following the manufacturer’s recommended protocol.
Definition of hypertriglyceridemia and grouping methodIn the 2016 Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adults [20], fasting TG ≥1.7 mmol/L was referred to as HTG. The Expert Panel’s statement from 2019 [8] states that the definition of PHTG is postprandial TG >2.5 mmol/L following an OFTT meal. Depending on the outcomes of the OFTT, our subjects were separated into three groups: 1) normal fat tolerance (NFT) group (TG at 0 h <1.7 mmol/L and TG at any time point <2.5 mmol/L); 2), IFT group (TG at 0 h <1.7 mmol/L and TG at any time point >2.5 mmol/L); and 3), and HTG group (TG at 0 h ≥1.7 mmol/L).
Statistical analysisWe used SPSS version 21.0 (IBM Inc, Armonk, NY, USA) for statistical analysis. The mean and standard deviation (SD) were used to depict normally distributed data. The median (interquartile range) was used to represent non-normally distributed data. For comparisons between the three groups, one-way ANOVA was used if the data were normally distributed. Otherwise, a non-parametric test was used. Lipid levels during the OFTT were compared using repeated measures of ANOVA. The trapezoidal segmentation method was used to calculate the area under the curve (AUC) for TG and NLRP3 levels and the increase in the AUC (∆AUC), compared with baseline levels. Pearson correlation analysis was used for normally distributed data, while Spearman correlation analysis was used for non-normally distributed data. The chi-square test was used for comparing differences between categorical variables. The relationship between serum NLRP3 levels and IFT and HTG was assessed using binary logistic regression analysis. The receiver operating characteristic (ROC) curve was used to analyze the sensitivity and specificity of NLRP3 for IFT and HTG diagnosis. A p value <0.05 was used to describe statistically significant differences.
Our study included 176 subjects: 56 were assigned to the NFT group, 60 to the IFT group, and 60 to the HTG group. There was no significant variation in age, sex ratio, or ApoA1 levels between the three groups (Table 1). The levels of BMI, WC, DBP, SUA, FBG, FINS, HOMA-IR, HOMA-β, TC, TG, LDL-C, ApoB, Non-HDL-C, TRLRs, and NLRP3 significantly differed among the groups (p < 0.05), and increased progressively with the decrease in lipid tolerance. The level of SBP in the IFT and HTG groups was higher than that in the NFT group, and the level of HDL-C was lower than that in the NFT group (p < 0.05).
| Group | NFT (56) | IFT (60) | HTG (60) | p |
|---|---|---|---|---|
| Age (years) | 42.91 ± 11.79 | 45.32 ± 12.19 | 44.68 ± 10.96 | 0.519 |
| Male sex, n (%) | 29 (51.79) | 32 (53.33) | 38 (63.33) | 0.390 |
| BMI (kg/m2) | 23.57 ± 2.86 | 26.79 ± 3.26*** | 29.59 ± 3.77***‡ | <0.001 |
| WC (cm) | 81.41 ± 9.12 | 90.53 ± 8.91*** | 96.97 ± 10.18***‡ | <0.001 |
| SBP (mmHg) | 120.89 ± 13.93 | 128.02 ± 13.61* | 132.57 ± 12.62*** | <0.001 |
| DBP (mmHg) | 74.18 ± 8.41 | 78.07 ± 8.75* | 84.12 ± 8.78***‡ | <0.001 |
| SUA (μmol/L) | 283.5 ± 60.81 | 329.12 ± 80.19* | 382.55 ± 98.46***† | <0.001 |
| FBG (mmol/L) | 4.95 ± 0.4 | 5.18 ± 0.51* | 5.36 ± 0.47***† | <0.001 |
| FINS (μIU/mL) | 6.82 (5.13–9.59) | 11.5*** (7.31–14.21) | 13.725***† (9.76–21.96) | <0.001 |
| HOMA-IR | 1.52 (1.09–2.11) | 2.59*** (1.67–3.26) | 3.34***† (2.34–4.89) | <0.001 |
| HOMA-β | 106.05 (73.88–137.39) | 138.95* (95.27–206.35) | 142.96*** (105.88–211.80) | <0.001 |
| TC (mmol/L) | 4.19 ± 0.63 | 4.78 ± 0.96*** | 5.37 ± 0.95***† | <0.001 |
| TG (mmol/L) | 0.82 ± 0.26 | 1.36 ± 0.23*** | 3.11 ± 0.94***‡ | <0.001 |
| HDL-C (mmol/L) | 1.37 ± 0.29 | 1.16 ± 0.21*** | 1.14 ± 0.26*** | <0.001 |
| LDL-C (mmol/L) | 2.55 ± 0.48 | 3.13 ± 0.73*** | 3.49 ± 0.68***† | <0.001 |
| Non-HDL-C (mmol/L) | 2.82 ± 0.57 | 3.62 ± 0.86*** | 4.23 ± 0.78***‡ | <0.001 |
| TRLRs (mmol/L) | 0.27 ± 0.16 | 0.49 ± 0.18*** | 0.74 ± 0.23***‡ | <0.001 |
| ApoA1 (g/L) | 1.44 ± 0.24 | 1.38 ± 0.22 | 1.37 ± 0.21 | 0.146 |
| ApoB (g/L) | 0.64 ± 0.13 | 0.86 ± 0.2*** | 0.97 ± 0.2***† | <0.001 |
| NLRP3 (pg/mL) | 55.77 ± 12.24 | 68.81 ± 14.14*** | 86.52 ± 15.26***‡ | <0.001 |
* p < 0.05 versus the NFT group; *** p < 0.001 versus the NFT group; † p < 0.05 versus the IFT group; ‡ p < 0.001 versus the IFT group.
Abbreviations: NFT, normal fat tolerance; IFT, impaired fat tolerance; HTG, hypertriglyceridemia; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; SUA, serum uric acid; FBG, fasting blood glucose; FINS, fasting insulin; HOMA-IR, homeostasis model assessment of insulin resistance; HOMA-β, homeostasis model assessment for β-cell function index; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; non-HDL-C, non-high-density lipoprotein-cholesterol; TRLRs, triglyceride-rich lipoprotein remnants; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; NLRP3, NOD-like receptor protein 3.
Serum TC, HDL-C, LDL-C, ApoA1, and ApoB levels did not change substantially after the high-fat meal, while TG levels dramatically increased in all three groups (Fig. 1a, d–h). In the NFT group, the TG level peaked 2 h after the meal; in the IFT group, the TG level peaked 4 h after the meal; and in the HTG group, the TG level continued to increase for 6 h after the meal. AUCTG and ∆AUCTG significantly differed among the three groups (p < 0.05), and increased progressively with the decrease in lipid tolerance (Fig. 1 b, c). At any time point, the levels of TG, TC, LDL-C, and ApoB significantly differed among the three groups (p < 0.05), and increased progressively with the decrease in lipid tolerance (p < 0.05). The level of HDL-C in the IFT and HTG groups was lower than that in the NFT group (p < 0.05; Table 2).
Comparison of lipid profiles and NLRP3 levels before and after the oral fat tolerance test (OFTT) meal
* p < 0.05 versus the NFT group; *** p < 0.001 versus the NFT group; # p < 0.05 versus the IFT group; ### p < 0.001 versus the IFT group.
Abbreviations: NLRP3, NOD-like receptor protein 3; AUCTG, area under the curve for TG; AUCNLRP3, area under the curve for NLRP3; ∆AUCTG, change in the AUCTG; ∆AUCNLRP3, change in the AUCNLRP3;TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B.
| 0 h | 2 h | 4 h | 6 h | |
|---|---|---|---|---|
| TG (mmol/L) | ||||
| NFT (56) | 0.82 ± 0.26 | 1.49 ± 0.5‡‡‡ | 1.47 ± 0.46‡‡‡ | 1.35 ± 0.48 |
| IFT (60) | 1.36 ± 0.23*** | 2.54 ± 0.64***‡‡‡ | 3.43 ± 0.62***‡‡‡### | 3.29 ± 1.2***‡‡‡# |
| HTG (60) | 3.11 ± 0.94***††† | 4.37 ± 1.3***†††‡‡‡ | 5.73 ± 1.85***†††‡‡‡### | 6.27 ± 2.57***†††‡‡‡###$ |
| TC (mmol/L) | ||||
| NFT (56) | 4.19 ± 0.63 | 4.13 ± 0.61 | 4.12 ± 0.62 | 4.29 ± 0.63###$$$ |
| IFT (60) | 4.78 ± 0.96* | 4.77 ± 0.92*** | 4.81 ± 0.91*** | 4.91 ± 0.99*‡###$ |
| HTG (60) | 5.37 ± 0.95***† | 5.33 ± 0.95***† | 5.49 ± 0.99***†††‡### | 5.68 ± 1.02***†††‡‡‡###$$$ |
| HDL-C (mmol/L) | ||||
| NFT (56) | 1.37 ± 0.29 | 1.37 ± 0.29 | 1.31 ± 0.28‡‡‡### | 1.35 ± 0.29 |
| IFT (60) | 1.16 ± 0.21*** | 1.16 ± 0.19*** | 1.1 ± 0.18***‡‡‡### | 1.07 ± 0.18***‡‡‡###$ |
| HTG (60) | 1.14 ± 0.26*** | 1.15 ± 0.26*** | 1.09 ± 0.26***‡‡‡### | 1.06 ± 0.27***‡‡‡###$$$ |
| LDL-C (mmol/L) | ||||
| NFT (56) | 2.55 ± 0.48 | 2.47 ± 0.47‡‡‡ | 2.44 ± 0.45‡‡‡ | 2.52 ± 0.46$$$ |
| IFT (60) | 3.13 ± 0.73*** | 3.06 ± 0.69***‡‡‡ | 2.97 ± 0.66***‡‡‡### | 3.06 ± 0.70***‡$$$ |
| HTG (60) | 3.49 ± 0.68***† | 3.38 ± 0.65***†‡‡‡ | 3.31 ± 0.65***†‡‡‡### | 3.35 ± 0.66***†‡‡‡ |
| ApoA1 (g/L) | ||||
| NFT (56) | 1.44 ± 0.24 | 1.43 ± 0.24 | 1.42 ± 0.24 | 1.46 ± 0.25#$$$ |
| IFT (60) | 1.38 ± 0.22 | 1.35 ± 0.21 | 1.34 ± 0.18‡ | 1.37 ± 0.2$ |
| HTG (60) | 1.37 ± 0.21 | 1.34 ± 0.21 | 1.36 ± 0.22 | 1.38 ± 0.23# |
| ApoB (g/L) | ||||
| NFT (56) | 0.64 ± 0.13 | 0.63 ± 0.12 | 0.62 ± 0.12‡ | 0.65 ± 0.13#$ |
| IFT (60) | 0.86 ± 0.2*** | 0.84 ± 0.19*** | 0.81 ± 0.19***‡‡‡### | 0.84 ± 0.2***$$$ |
| HTG (60) | 0.97 ± 0.2***† | 0.96 ± 0.2***†‡ | 0.93 ± 0.2***†‡‡‡### | 0.94 ± 0.21***†‡‡‡ |
| NLRP3 (pg/mL) | ||||
| NFT (56) | 55.77 ± 12.24 | 64.00 ± 13.16‡‡‡ | 56.21 ± 11.47 ### | 51.08 ± 8.25‡###$ |
| IFT (60) | 68.81 ± 14.14*** | 83.20 ± 16.39***‡‡‡ | 67.68 ± 12.88***### | 63.54 ± 10.67***‡###$ |
| HTG (60) | 86.52 ± 15.26***††† | 108.82 ± 20.14***†††‡‡‡ | 93.61 ± 17.16***†††‡‡‡### | 83.84 ± 14.15***†††###$$$ |
* p < 0.05 versus the NFT group; *** p < 0.001 versus the NFT group; † p < 0.05 versus the IFT group; ††† p < 0.001 versus the IFT group; ‡ p < 0.05 versus 0 h in the same group; ‡‡‡ p < 0.001 versus 0 h in the same group; # p < 0.05 versus 2 h in the same group; ### p < 0.001 versus 2 h in the same group; $ p < 0.05 versus 4 h in the same group; $$$ p < 0.001 versus 4 h in the same group.
Abbreviations: NFT, normal fat tolerance; IFT, impaired fat tolerance; HTG, hypertriglyceridemia; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; NLRP3, NOD-like receptor protein 3.
Following an OFTT meal, NLRP3 levels increased considerably in all three groups, peaking 2 h later. (Fig. 1i). At any time point, the level of NLRP3 significantly differed among the three groups (p < 0.05), and increased progressively with the decrease in lipid tolerance (p < 0.05). AUCNLRP3 significantly differed among the three groups (p < 0.05), and increased progressively with the decrease in lipid tolerance (Fig. 1j). ∆AUCNLRP3 in the HTG group was higher than that in the NFT and IFT groups (p < 0.05; Fig. 1k).
Comparison of baseline data of different fasting serum NLRP3 level groupsThe subjects were separated into four groups based on their fasting serum NLRP3 quartile (Quartile 1 [Q1]: 38.86–54.82 pg/mL; Quartile 2 [Q2]: 54.82–68.85 pg/mL; Quartile 3 [Q3]: 68.85–86.84 pg/mL; Quartile 4 [Q4]: 86.84–119.92 pg/mL). The incidence of NFT decreased as NLRP3 quartiles increased, while IFT and HTG increased; changes in HOMA-β and ApoA1 were not statistically different (p > 0.05). HDL-C showed a gradual decreasing trend, and other glucolipid metabolism and anthropometric indicators showed a gradual increasing trend (p < 0.05; Table 3). Specifically, TG, AUCTG, and ∆AUCTG showed a gradual increasing trend, indicating that the serum NLRP3 level affected not only fasting TG but also postprandial TG.
| Group | Q1 (44) | Q2 (44) | Q3 (44) | Q4 (44) | p |
|---|---|---|---|---|---|
| BMI (kg/m2 ) | 24.8 ± 3.48 | 26.33 ± 3.97 | 27.37 ± 3.94* | 28.39 ± 4.28***# | <0.001 |
| WC (cm) | 83.99 ± 10.28 | 88.59 ± 9.43* | 91.67 ± 11.65* | 95.09 ± 11.24***# | <0.001 |
| SBP (mmHg) | 123.75 ± 13.52 | 123.93 ± 14.35 | 129.14 ± 14.83 | 132.39 ± 12.29*# | 0.008 |
| DBP (mmHg) | 77.16 ± 9.33 | 76.5 ± 10.09 | 78.86 ± 9.67 | 83.05 ± 7.77*#† | 0.005 |
| SUA (μmol/L) | 302.52 ± 70.59 | 303.91 ± 75.67 | 345.16 ± 87.71*# | 379.68 ± 104.38***### | <0.001 |
| FBG (mmol/L) | 5.07 ± 0.44 | 5.06 ± 0.48 | 5.21 ± 0.52 | 5.36 ± 0.46*# | 0.011 |
| FINS (μIU/mL) | 8.66 (5.30,13.33) | 9.53 (6.86,12.25) | 11.22* (8.10,17.05) | 12.93* (9.04,12.04) | 0.003 |
| HOMA-IR | 1.86 (1.17,3.20) | 2.15 (1.47,2.84) | 2.63* (1.82,4.22) | 2.97*# (2.15,4.02) | 0.001 |
| HOMA-β | 118.04 (75.29,172.05) | 125.47 (100.82,156.14) | 144.51 (96.64,210.53) | 137.36 (86.32,218.17) | 0.269 |
| TC (mmol/L) | 4.34 ± 0.8 | 4.4 ± 0.81 | 5.25 ± 1.1***### | 5.19 ± 0.84***### | <0.001 |
| TG (mmol/L) | 0.74 (0.61,1.06) | 1.19* (0.95,1.47) | 1.61***# (1.31,2.47) | 3.20***###† (1.68,4.14) | <0.001 |
| AUCTG (mmol/L*6h) | 10.14 ± 5.33 | 14.96 ± 7.15* | 19.96 ± 6.81***# | 28.28 ± 11.54***###† | <0.001 |
| ∆AUCTG (mmol/L*6h) | 4.56 (2.11, 6.05) | 5.87 (3.78, 9.96) | 8.27*** (5.83,10.82) | 10.31*** (5.46,13.91) | <0.001 |
| HDL-C (mmol/L) | 1.33 ± 0.29 | 1.23 ± 0.28 | 1.19 ± 0.24* | 1.13 ± 0.23*** | 0.003 |
| LDL-C (mmol/L) | 2.69 ± 0.62 | 2.78 ± 0.6 | 3.49 ± 0.8***### | 3.32 ± 0.61***### | <0.001 |
| Non-HDL-C (mmol/L) | 3 ± 0.78 | 3.17 ± 0.73 | 4.06 ± 0.94***### | 4.07 ± 0.77***### | <0.001 |
| TRLRs (mmol/L) | 0.32 ± 0.21 | 0.38 ± 0.16 | 0.57 ± 0.22***### | 0.75 ± 0.24***###‡ | <0.001 |
| ApoA1 (g/L) | 1.46 ± 0.22 | 1.38 ± 0.23 | 1.38 ± 0.22 | 1.35 ± 0.23 | 0.163 |
| ApoB (g/L) | 0.7 ± 0.19 | 0.74 ± 0.19 | 0.95 ± 0.23***### | 0.92 ± 0.19***### | <0.001 |
| Degree of impaired fat tolerance | |||||
| NFT, n (%) | 30 (68.18) | 18 (40.91) | 5 (11.36) | 3 (6.82) | <0.001 |
| IFT, n (%) | 11 (25) | 19 (43.18) | 20 (45.45) | 10 (22.73) | |
| HTG, n (%) | 3 (6.82) | 7 (15.91) | 19 (43.18) | 31 (70.45) |
* p < 0.05 versus the Q1 group; *** p < 0.001 versus the Q1 group; # p < 0.05 versus the Q2 group; ### p < 0.001 versus the Q2 group; † p < 0.05 versus the Q3 group; ‡ p < 0.001 versus the Q3 group.
Abbreviations: BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; SUA, serum uric acid; FBG, fasting blood glucose; FINS, fasting insulin; HOMA-IR, homeostasis model assessment of insulin resistance; HOMA-β, homeostasis model assessment for β-cell function index; TC, total cholesterol; TG, triglyceride; AUCTG, area under the curve for TG; ∆AUCTG, change in the AUCTG; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; non-HDL-C, non-high-density lipoprotein-cholesterol; TRLRs, triglyceride-rich lipoprotein remnants; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; NFT, normal fat tolerance; IFT, impaired fat tolerance; HTG, hypertriglyceridemia.
Fasting serum NLRP3 levels were correlated with all indicators except for age (p < 0.05; Table 4), and the strongest correlation was with TG (r = 0.820, p < 0.001). ∆AUCNLRP3 was correlated with BMI, WC, DBP, SUA, FINS, HOMA-IR, TG, ∆AUCTG, and TRLRs (p < 0.05), with TG being most tightly correlated (r = 0.277, p < 0.001).
| NLRP3 | ∆AUCNLRP3 | |||
|---|---|---|---|---|
| r | p | r | p | |
| Age (years) | 0.024 | 0.753 | –0.014 | 0.854 |
| BMI (kg/m2 ) | 0.385 | <0.001 | 0.228 | 0.002 |
| WC (cm) | 0.416 | <0.001 | 0.176 | 0.019 |
| SBP (mmHg) | 0.259 | 0.001 | 0.124 | 0.102 |
| DBP (mmHg) | 0.287 | <0.001 | 0.212 | 0.005 |
| SUA (μmol/L) | 0.387 | <0.001 | 0.170 | 0.024 |
| FBG (mmol/L) | 0.231 | 0.002 | 0.100 | 0.189 |
| FINS (μIU/mL) | 0.333 | <0.001 | 0.203 | 0.007 |
| HOMA-IR | 0.361 | <0.001 | 0.211 | 0.005 |
| HOMA-β | 0.172 | 0.005 | 0.128 | 0.091 |
| TC (mmol/L) | 0.441 | <0.001 | 0.066 | 0.385 |
| TG (mmol/L) | 0.820 | <0.001 | 0.277 | <0.001 |
| ∆AUCTG (mmol/L*6h) | 0.338 | <0.001 | 0.195 | 0.009 |
| HDL-C (mmol/L) | –0.308 | <0.001 | –0.122 | 0.108 |
| LDL-C (mmol/L) | 0.441 | <0.001 | 0.061 | 0.422 |
| Non-HDL-C (mmol/L) | 0.552 | <0.001 | 0.104 | 0.169 |
| TRLRs (mmol/L) | 0.713 | <0.001 | 0.197 | 0.009 |
| ApoA1 (g/L) | –0.163 | 0.031 | –0.049 | 0.521 |
| ApoB (g/L) | 0.483 | <0.001 | 0.132 | 0.08 |
Abbreviations: NLRP3, NOD-like receptor protein 3; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; SUA, serum uric acid; FBG, fasting blood glucose; FINS, fasting insulin; HOMA-IR, homeostasis model assessment of insulin resistance; HOMA-β, homeostasis model assessment for β-cell function index; TC, total cholesterol; TG, triglyceride; ∆AUCTG, change in the AUCTG; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; non-HDL-C, non-high-density lipoprotein-cholesterol; TRLRs, triglyceride-rich lipoprotein remnants; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B.
A binary logistic regression model was applied to analyze the relationship between fasting serum NLRP3 levels and IFT and HTG, using the NFT and IFT groups as the reference groups, respectively (Table 5). After adjusting for sex, age, BMI, WC, UA, SBP, and DBP, the fasting serum NLRP3 was positively correlated with IFT and HTG (for IFT, odds ratio [OR]: 1.079 [1.037–1.123], p < 0.001; for HTG, OR: 1.085 [1.049–1.123], p < 0.001). After further adjustment for FBG, FINS, TC, HDL-C, LDL-C, ApoA1, and ApoB, fasting serum NLRP3 was still positively correlated with IFT and HTG (for IFT, OR: 1.067 [1.017–1.123], p = 0.014; for HTG, OR: 1.079 [1.042–1.117], p < 0.001).
| NFT vs. IFT | IFT vs. HTG | |||
|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | |
| Model 1 | 1.076 (1.041–1.112) | <0.001 | 1.082 (1.049–1.115) | <0.001 |
| Model 2 | 1.079 (1.037–1.123) | <0.001 | 1.085 (1.049–1.123) | <0.001 |
| Model 3 | 1.067 (1.013–1.123) | 0.014 | 1.079 (1.042–1.117) | <0.001 |
Model 1: crude; Model 2: adjusted for age, sex, BMI, WC, SUA, SBP, and DBP; Model 3: adjusted for Model 2 + FPG, FINS, TC, HDL-C, LDL-C, ApoA1, and ApoB.
Abbreviations: NFT, normal fat tolerance; IFT, impaired fat tolerance; HTG, hypertriglyceridemia; OR, odds ratio.
The ROC curve was used to evaluate the sensitivity and specificity of fasting serum NLRP3 in the diagnosis of IFT and HTG. The results showed that the AUC of fasting serum NLRP3 in diagnosing IFT was 0.763 (95%CI 0.677–0.849), the sensitivity was 63.3%, the specificity was 76.8%, and the cut-off value was 63.135 pg/mL (Fig. 2a). The AUC of fasting serum NLRP3 in diagnosing HTG was 0.803 (95%CI 0.724–0.882), the sensitivity was 76.70%, the specificity was 80.0%, and the cut-off value was 79.32 pg/mL (Fig. 2b).
ROC curve analysis of the sensitivity and specificity of NLRP3 to diagnose IFT (a) or HTG (b).
Abbreviations: IFT, impaired fat tolerance; HTG, hypertriglyceridemia; ROC, receiver operating characteristic
Our study recruited 176 volunteers with different fat tolerances who underwent the OFTT. Our study showed that with the aggravation of lipid tolerance impairment, serum NLRP3 levels gradually increased at any time point before and after high-fat meals. The fasting serum NLRP3 level was shown to be positively linked with IFT and HTG using logistic regression analysis. The ROC curve showed that the fasting serum NLRP3 level was an effective biomarker for IFT and HTG diagnosis. Our results suggest that even when fasting TG was within the normal range, the level of NLRP3 began to rise after meals in the population with elevated TG (IFT group). Measuring the serum NLRP3 level may help to detect TG levels at an early stage, enabling the early diagnosis and/or prevention of HTG.
In cases of decreased lipid tolerance, from the NFT group to the HTG group, we found that TG levels gradually increased before and after the OFTT meal, at any time point. TG levels increased considerably after the OFTT meal in the IFT and HTG groups compared to the NFT group, with a delayed peak time. The postprandial TG level was higher in the IFT group than in the NFT group. Additionally, the fasting TG level of the IFT group was higher than that of the NFT group, suggesting that PHTG should be tested even when fasting TG is within the normal range. It is important to note that most people only fast for a short period of time each day, usually in the early morning. Because of this, plasma lipid concentrations measured while not fasting may more accurately represent an individual’s metabolic status than those observed when fasting [21, 22]. Kolovou et al. recommended that OFTT screening for PHTG be considered when fasting and postprandial TG values are 1–2 mmol/L and 1.3–2.3 mmol/L, respectively [8]. Most of the possible atherosclerotic alterations in TG-rich residual lipoproteins occur in the postprandial state [23]. Even when fasting TG levels are normal in people with diabetes mellitus, PHTG has been associated with an increase in morbidity and mortality from atherosclerotic cardiovascular disease (ASCVD) [24]. According to previous research, high-fat meals and high-fat, high-carbohydrate (HFHC) meals induce acute postprandial inflammation and oxidative stress. Considerable research has already examined the impact of a single high-fat meal on postprandial inflammation, and many report that systemic inflammatory markers significantly increase following high-fat meals [25-28]. Fatima et al. [29] investigated the OFTT with 86 subjects and found NLRP3 to be the “hub gene” of acute inflammatory response after high-fat meals, based on weighted gene co-expression network analysis. According to our research, serum NLRP3 levels were significantly raised in all three groups after consuming an OFTT meal, with NLRP3 levels peaking at 2 h postprandial and progressively declining at 4 h and 6 h. This result is considered to be related to the increased production of reactive oxygen species (ROS) activated by oxidative stress after a high-fat diet. ROS acts as the upstream factor of NLRP3 inflammasome activation, thus activating the NLRP3 inflammasome [30]. For example, Patel et al. selected subjects with normal weight (BMI = 23.3 ± 0.4 kg/m2) and obese subjects (BMI = 35.5 ± 2.3 kg/m2) for their oral HFHC test, they found that ROS in peripheral blood mononuclear cells (MNCs) of subjects with normal weight peaked 2 h after meals and decreased to the fasting level after 3 h, while ROS in obese people was always high within 3 h after meals [31]. However, we did not find any difference in the trend of ROS change among people with different BMIs. Ghanim et al. recruited 10 subjects with normal body weight (BMI = 22.8 ± 0.7 kg/m2) to eat HFHC meals and found that ROS in MNCs peaked 2 h after meals and gradually decreased after 3 h and 5 h [26].
We also found that, as lipid tolerance decreased, the NLRP3 level increased gradually before and at any time point following an OFTT meal. With increasing NLRP3 quartiles, the incidence of IFT and HTG gradually increased. Pearson correlation analysis revealed that fasting serum NLRP3 and ∆AUCNLRP3 was significantly linked with fasting TG and ∆AUCTG. We conclude that serum NLRP3 levels are linked to the degree of IFT. The NLRP3 inflammasome has been linked to lipid metabolism in several studies. However, previous research has mainly focused on how the NLRP3 inflammasome affects the accumulation of lipids in animal tissues or cells. For example, inhibition of the ROS–NLRP3 inflammasome can ameliorate dyslipidemia, reduce the TG level, and alleviate hepatic steatosis in diabetic mice [32]. There are few studies on the specific mechanisms of NLRP3 and lipid metabolism-related diseases. Serum NLRP3 effects on fasting and postprandial TG levels may be related to the following factors: NLRP3 inflammatorome activation promotes the activation of procaspase-1 into mature caspase-1. Caspase-1–/– mice were found to have lower serum TG levels after lipid loading by decreasing intestinal chylomicron-TG production and liver very low-density lipoprotein (VLDL)-TG production [33]. Another study discovered that Caspase-1–/– mice could promote lipoprotein lipase (LPL)-mediated clearance of TG-rich lipoproteins by inhibiting ApoC1 expression independently of IL-1 family signaling, resulting in lower serum TG levels [34]. Therefore, it is speculated that increased caspase-1 expression can increase intestinal chylomicron-TG production and liver VLDL-TG production, inhibit LPL activity, and delay TG clearance, thus increasing the serum TG levels. Moreover, activation of the NLRP3 inflammasome promotes the excessive release of inflammatory factors such as IL-1β, which affects insulin signaling and leads to insulin resistance [35]. Insulin resistance can lead to: 1) promoting hepatic de novo lipogenesis and increasing free fatty acid (FFA) release from adipocytes; inhibiting the degradation of ApoB; and increasing the stability of ApoB. Thus, VLDL-TG synthesis and secretion in the liver, as well as serum TG levels, were increased [36-40]. 2) inhibiting LPL activity in adipocytes, which slows down the metabolism of TG-rich lipoproteins and thus increases serum TG levels [38-40]. Furthermore, activation of NLRP3 inflammasomes can induce adipogenesis [41], inhibit the browning of white adipose tissue [42], increase the release of FFA from adipocytes, promote the synthesis and secretion of VLDL-TG in the liver, and further increase the serum TG level, leading to IFT and HTG.
To the best of our knowledge, this is the first study to investigate changes in serum NLRP3 levels before and after high-fat meals and to correlate IFT and HTG in people with different fat tolerances. We found that fasting serum NLRP3 is an important predictor of IFT and HTG.
This research has several restrictions. First, we used cross-sectional data, which did not allow for inference of causal relationships between serum NLRP3 levels and TG. Second, the limited sample size could have impacted the statistical validity of the results. The function of NLRP3 in hypertriglyceridemia needs to be clarified by additional clinical trials and prospective research.
In conclusion, by comparing the levels of serum NLRP3 in people with different lipid tolerance levels, we discovered that fasting serum NLRP3 was both a useful biomarker for IFT and HTG diagnosis as well as an independent risk factor for both conditions. With the increase in serum NLRP3 levels, the risks of IFT and HTG gradually increase. IFT can occur when fasting TG is normal; however, monitoring serum NLRP3 can detect IFT early, which permits the diagnosis, treatment, and prevention of lipid metabolism-related diseases.
We sincerely thank the faculty of the Hebei Endocrine Metabolic Disease Research Center for their support of this study. We also thank Editage (www.editage.cn) for English language editing.
None of the authors declare any potential conflicts of interest associated with this study.
