ABSTRACT
We investigate the role of homeostatic mechanisms involved in acute, postprandial nutrient metabolism and nutrient-induced systemic inflammation in CKD presence and progression in Metabolic dysfunction-associated steatohepatitis (MASH). We assessed postprandial incretins (GLP-1 and GIP), intestinotropic hormone GLP-2, endotoxin LPS, Zonulin (a marker of intestinal permeability), hepatokines, adipokines and NF-kB activation in circulating MNCs during a meal tolerance test in 52 biopsy proven MASH patients randomized to curcumin Meriva or placebo and 26 matched controls. At baseline, MASH-CKD had a lower GLP-2 response and a 2-fold higher postprandial LPS and NF-kB activation in MNCs than MASH patients without CKD, but similar remaining postprandial or fasting parameters. Postprandial IAUC GLP-2 predicted the presence of CKD in MASH (OR = 0.43, 95%CI:0.32-0.80, p = 0.008) independently of liver histology and traditional risk factors. After 72 weeks, changes in IAUC GLP-2 independently predicted the presence of CKD (OR = 0.49, 95%CI:0.21-0.73, p = 0.010) and eGFR changes [β(SE) = 0.510(0.007, p = 0.006] at end-of-treatment, In MASH, an impaired GLP-2 response to meals is associated with intestinal barrier dysfunction, endotoxemia and NF-kB-mediated systemic inflammation and may promote renal dysfunction and CKD. These data provide the rationale for evaluating GLP-2 analogues in MASH-related CKD.
KEYWORDS: Steatohepatitis, NF-kB, postprandial, eGFR, albuminuria, chemokine
Main text
Metabolic dysfunction-associated steatohepatitis (MASH), previously called Nonalcoholic steatohepatitis, NASH)1,2 is a multisystem disease with an increased risk of chronic kidney disease (CKD)3–5 and poor renal outcomes: consistently, MASH is the most rapidly growing indication for simultaneous liver-kidney transplantation.6 Mechanisms underlying kidney disease in MASH are unclear and none of the proposed MASH pharmacological treatments to date provides nephro-protection.7
MASH pathogenesis is multifactorial and involves genetic, epigenetic, nutritional, metabolic and inflammatory factors.8–10
The postprandial phase is the metabolic period during and after a meal (6–8 h), which involves the digestion and absorption of ingested nutrients and is characterized by an increase in glycemia, lipidemia and gut-derived endotoxemia, which promote systemic low-grade inflammation and are a pathogenic hallmark of metabolic syndrome.11 Consistently, the postprandial state is emerging as a key determinant of cardio-metabolic health.11
Although metabolic assessments are conventionally performed in the fasting state, in Western countries individuals spend most of the daytime in the postprandial phase: hypothesizing repetitive postprandial inflammatory bouts could promote CKD development and progression in MASH, we investigated
1)the link between postprandial nutrient homeostasis, nutritional inflammation and the development of CKD in MASH;
2)the effect of a novel, enhanced curcumin formulation on the above mechanisms and on kidney disease in MASH. Curcumin is a known stimulator of Glucagon-like Peptides secretion by enteroendocrine cells via different molecular mechanisms12–14
Methods
We took advantage of a recent multicenter randomized trial assessing the effect of enhanced phospholipid curcumin formulation Meriva in MASH15: : in this trial, 52 biopsy-proven MASH patients were randomized to Meriva 2 g/d or placebo for 72 weeks, after stratification for baseline diabetes, liver fibrosis stage (F0–2 vs. F3–4), CKD (presence/absence), and obesity status. Chemical characteristics of curcumin Meriva formulation, trial design, methods and protocol and patient eligibility criteria are described in supplementary material and detailed elsewhere.15
All MASH patients underwent a standardized 8-hr oral mixed tolerance test at entry and at the end-of-treatment (EOT), with extensive metabolic and inflammatory assessment (Table 1), including measurement of circulating:
incretins (GIP and GLP-1) and GLP-2, a key intestinotrophic hormone released from enteroendocrine L cells;
zonulin, a marker of gut permeability, and lipopolysaccharide (LPS) a marker of endotoxemia,
hepatokine Fibroblast Growth Factor(FGF)-21;
adipokine adiponectin16;
the proinflammatory transcription factor Nuclear Factor (NF)-kB activation in blood MNCs and in the liver. NF-kB is a pivotal mediator of metabolic and nutritional inflammation, is activated by plasma glucose, non-esterified fatty acids (NEFAs) and LPS17 and inhibited by GLP-2.18
Table 1.
Baseline and end-of-treatment (EOT) demographic, clinical, biochemical and histological characteristics of included MASH patients grouped according the the presence of chronic kidney disease (CKD)(n = 52) and 26 age-, gender-, BMI- and diabetes-matched controls.
| Controls (n = 26) | MASH CKD at baseline(n = 32) |
MASH no CKD at baseline(n = 20) |
P for between-group changes in | |||
|---|---|---|---|---|---|---|
| Baseline | EOT | Baseline | EOT | MASH | ||
| MASH patients on active treatment n (%) | 0(0%) | 16(50%) | 16(50%) | 10(50%) | 10(50%) | 0.897 |
| Demographics | ||||||
| Age (years) | 54 (12) | 55 (11) | 56 (11) | 53 (10) | 54 (10) | 0.638 |
| Male | 13 (50%) | 15 (47%) | 15(47%) | 11 (55%) | 11 (55%) | 0.489 |
| Caucasian white race | 52(100%) | 30(94%) | 30(94%) | 19(95%) | 19(95%) | 0.528 |
| Weight status | ||||||
| Obesity Overweight |
18(70%) 8 (30%) |
23 (72%) 9 (28%) |
23 (72%) 9 (28%) |
14(70%) 6 (30%) |
14 (70%) 6 (30%) |
0.781 0.613 |
| Comorbidities | ||||||
| Type 2 diabetes | 13(50%) | 16(50%) | 16 (50%) | 9(45%) | 9 (45%) | 0.569 |
| Hyperlipidaemia* | 15 (58%) | 16 (50%) | 16(50%) | 12 (60%) | 12 (60%) | 0.478 |
| Hypertension† | 19 (73%) | 18 (56%) | 18 (56%) | 12(60%) | 12 (60%) | 0.396 |
| Cardiovascular disease | 0(0%) | 1 (3%) | 1 (3%) | 1 (5%) | 1 (5%) | 0.791 |
| Current smoker | 5 (19%) | 3 (9%) | 3 (9%) | 4 (20%) | 4 (20%) | 0.582 |
| Medications | ||||||
| Anti-diabetic | ||||||
| Metformin | 15(58%) | 14 (44%) | 14 (44%) | 10 (50%) | 10 (50%) | 0.419 |
| Sulfonylurea | 1 (4%) | 1 (3%) | 1 (3%) | 1 (5%) | 1 (5%) | 0.649 |
| DPP-IV inhibitors | 4 (9%) | 3 (9%) | 3 (9%) | 2 (10%) | 2 (10%) | 0.82 |
| Insulin | 9(28%) | 9(28%) | 9 (28%) | 6(30%) | 7 (35%) | 0.573 |
| Anti-lipidaemic | ||||||
| Statins PUFA Fibrates |
10 (38%) 4 (15%) 1 (4%) |
11 (34%) 3 (9%) 1 (3%) |
11 (34%) 3 (9%) 1 (3%) |
7 (35%) 2 (10%) 0 (0%) |
7 (35%) 2 (10%) 0 (0%) |
0.396 0.847 0.840 |
| Anti-hypertensive | ||||||
| ACEI/ARB | 14 (54%) | 16 (50%) | 16 (50%) | 11 (55%) | 11 (55%) | 0.893 |
| Others | 13 (50%) | 15 (47%) | 15 (47%) | 11 (55%) | 11 (55%) | 0.528 |
| Anti-platelet | 3 (12%) | 3 (9%) | 3 (9%) | 3 (15%) | 3 (15%) | 0.915 |
| Genetic polymorphisms | ||||||
| ApoE n(%) | ||||||
| 2-3 3-3 3-4 |
5(20) 16(60) 5(20) |
7(22) 18(57) 7(21) |
- | 4(20) 11(55) 5(25) |
- | 0.895 0.724 0.812 |
| PNPLA3 C/G n(%) | ||||||
| C/C C/G G/G |
15(58) 8(30) 3(12) |
9(28) 14(44) 9(28) |
- | 5(25) 9(47) 6(28) |
- | 0.478 0.791 0.482 |
| TM6SF2 C/T n(%) | ||||||
| CC CT TT |
22(85) 3(12) 1(3) |
25(79) 5(15) 2(6) |
- | 14(72) 4(20) 2(8) |
- | 0.792 0.827 0.318 |
| Dietary habits | ||||||
| Total energy Intake (kcal/d) | 2372 (169) | 2501 (140) | 2398 (151) | 2569 (131) | 2402 (128) | 0.759 |
| Kcal/kg BW/d | 30 (1) | 31 (1) | 31 (1) | 30 (1) | 30 (1) | 0.792 |
| Alcohol (g/d) | 4.0 (0.6) | 4.2 (0.7) | 4.0 (0.7) | 4.0(0.9) | 3.8 (0.8) | 0.713 |
| Fat (% kcal/d) | 33.0 (0.9) | 35.1 (0.8) | 34.0 (0.9) | 34.2 (0.9) | 33.0 (1.0) | 0.594 |
| CHO (% kcal/d) | 47.2 (1.8) | 49.2 (1.1) | 48.2 (1.1) | 48.9(1.1) | 46.4 (1.0) | 0.573 |
| Fiber (g/d) | 28.9 (2.1) | 26.5 (2.2) | 27.9 (2.9) | 24.9 (1.9) | 25.2 (1.5) | 0.692 |
| Protein (% kcal/d) | 18.5 (0.8) | 16.1 (0.0) | 14.8 (0.7) | 15.1 (0.9) | 14.2 (0.9) | 0.429 |
| SFA (% total fat) | 33.6(0.8) | 34.9 (0.4) | 30.2 (0.4) | 33.4 (0.9) | 31.1 (0.7) | 0.712 |
| MUFA(% total fat) | 46.6 (1.4) | 47.8 (1.3) | 48.9 (1.1) | 46.2 (1.1) | 47.2 (1.4) | 0.639 |
| PUFA(% total fat) | 13.2 (1.1) | 12.1 (0.8) | 14.0 (0.9) | 11.3 (0.9) | 13.0 (1.2) | 0.498 |
| Physical activity (PA) categories | ||||||
| Light PA (min/d) | 169.9 (36.4) | 171.8 (31.5) | 171.8 (31.5) | 193.4 (38.9) | 193.4 (38.9) | 0.813 |
| Moderate PA (min/d) | 102.6 (23.2) | 98.6 (22.1) | 101.6 (18.1) | 99.7 (15.9) | 103.4 (14.7) | 0.849 |
| Vigorous PA (min/d) | 8.3 (8.2) | 8.1 (7.3) | 8.9 (6.3) | 9.2 (7.2) | 10.0 (9.1) | 0.728 |
| Sedentary time (min/d) | 782.7 (88.7) | 809.8 (80.4) | 856.7 (91.2) | 838.4 (91.3) | 864.9 (89.9) | 0.769 |
| Metabolic parameters | ||||||
| Weight (kg) | 101.9 (9.7) | 103.9 (15.9) | 101.7 (19.4) | 102.7 (18.5) | 100.2 (15.3) | 0.413 |
| Body-mass index (kg/m 2 | 30.0(2.1) | 33.4(3.1) | 33.1 (2.7) | 33.9 (3.2) | 33.3 (2.9) | 0.991 |
| Waist circumference (cm) | 95.3 (9.7) | 107.3 (12.1) | 104.7 (10.9) | 106.1 (10.2) | 105.1 (10.2) | 0.739 |
| HbA1c (%) | 6.01 (0.47) | 6.72 (0.51) | 6.01 (0.43)† | 6.68 (0.52) | 6.03 (0.43)† | 0.413 |
| HbA1c (%) in T2DM | 7.12 (0.79) | 7.01 (0.52) | 6.32 (0.44)† | 7.12 (0.56) | 6.25 (0.48)† | 0.693 |
| HOMA-IR | 5.7 (2.4) | 7.7 (4.9) | 6.4 (3.2)† | 7.9 (4.1) | 6.1 (3.1)† | 0.529 |
| Total cholesterol (mg/dL) | 175 (33) | 196 (43) | 167(38)† | 199 (41) | 162(37)† | 0.492 |
| LDL-C (mg/dL) | 118 (21) | 138 (24) | 122 (18)† | 136 (26) | 119 (19)† | 0.467 |
| HDL-C (mg/dL) | 43 (5) | 39 (4) | 46 (4)† | 37 (4) | 44 (4)† | 0.518 |
| oxLDL (IU/L) | 46.7(11.2) | 48.3 (17.5) | 39.9 (19.9) | 51.7 (21.8) | 43.7 (19.7) | 0.881 |
| hsC-reactive protein (mg/L) | 5.3 (3.8) | 6.7 (4.1) | 5.9 (3.1) | 6.4 (3.8) | 5.1 (3.9) | 0.772 |
| Systolic blood pressure (mm Hg) | 127 (14) | 130 (13) | 127 (11) | 133 (12) | 130 (11) | 0.594 |
| Diastolic blood pressure (mm Hg) | 76 (11) | 79 (11) | 78 (11) | 78 (9) | 75 (10) | 0.395 |
| Renal function | ||||||
| Creatinine (mg/dL) | 9.78 (0.20) | 1.21 (0.20) ¶ | 0.89 (0.18)† | 0.87 (0.17) | 0.94 (0.28)† | 0.128 |
| eGFR (mL/min/1.73 m2) | 96(4) | 82(8) ¶ | 88(8)† | 92(10) | 97(10)† | 0.216 |
| eGFR stage (mL/min/1.73 m2) | ||||||
| G1 (≥90 mL/min/1.73 m2) | 26 (100%) | 0 (0%) | 13 (41%)† | 20(100%) | 16(80%) | 0.009 |
| G2(60-89) | 0(0%) | 22 (69%) | 14 (44%) | 0(42%) | 3(15%) | 0.169 |
| G3a(45-59) | 0(0%) | 10 (31%) | 5 (16%) | 0 (0%) | 1 (5%) | 0.314 |
| G3b (30-44) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0.999 |
| G4 (15-29) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0.999 |
| G5(<15) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0.999 |
| Albuminuria(AER)(mg/day) | 43(25) | 192(55) # | 42(34)† | 20(9) | 61(22) | |
| A1 stage (<30 mg/day) | 0(0%) | 0(0%) | 20(62%)† | 20(100%) | 16(80%) | 0.160 |
| A2 stage (30-300) | 32(100%) | 32(100%) | 12(38%)† | 0(0%) | 4(20%) | 0.211 |
| A3 stage (>300) | 0(0%) | 0(0%) | 0(0%) | 0(0%) | 0(0%) | 0.999 |
| CKD (eGFR stage≥G2 and/or albuminuria stage≥A2) n (%) |
0 (0%) | 32 (100%)# | 12 (38%)‡ | 0(0%) | 4(20%) | 0.009 |
| Liver function | ||||||
| Alanine aminotransferase (U/L) | 21 (8) | 81 (28) | 39 (22)† | 73 (32) | 37 (27)† | 0.213 |
| Aspartate aminotransferase (U/L) | 17 (21) | 57 (21) | 26 (18)† | 54 (24) | 32 (20)* | 0.393 |
| γ-glutamyl transferase (U/L) | 29 (18) | 119 (68) | 73 (61)* | 112 (75) | 72 (59)* | 0.692 |
| Alkaline phosphatase (U/L) | 47 (21) | 87 (25) | 78 (29) | 86 (38) | 74 (39) | 0.479 |
| Total bilirubin (mg/dL) | 0.82 (0.24) | 0.82 (0.24) | 0.73 (0.21) | 0.70 (0.19) | 0.60 (0.18) | 0.394 |
| Albumin (g/dL) | 3.9 (0.7) | 3.7 (0.6) | 3.9 (0.6) | 3.6(0.5) | 4.0 (0.5) | 0.529 |
| Noninvasive markers of liver disease severity | ||||||
| Comuterized sonographic Hepato/Renal ratio | 0.92(0.31) | 2.62(0.74) | 1.12(0.47)† | 2.60(0.60) | 1.23(0.58) | 0.397 |
| Fatty liver Index | 51(8) | 99(12) | 71(13 | 91(21) | 79(18) | |
| Cytokeratin-18 fragments M30 (U/L) | 101.4(22.4) | 501.4(127.6) # | 312.4(109.1)† | 439.7(124.1) | 297.2(112.1)† | 0.284 |
| FIB-4 | 1.01(0.21) | 2.03(0.36) | 1.57(0.39)* | 1.97(0.33) | 1.56(0.41)* | 0.479 |
| APRI | 0.21(0.22) | 0.71(0.23) | 0.48(0.28)* | 0.78(0.29) | 0.51(0.20)* | 0.528 |
| Liver histology and Immmunohistochemistry | ||||||
| Definite MASH | 0(0%) | 32 (100%) | 21 (66%) | 20 (100%) | 11 (55%) | 0.460 |
| NAFLD activity score (0–8) | - | 7.7 (0·8) ¶ | 4.2 (0·6)‡ | 5.2 (0·9) | 3.6 (0·9)‡ | 0.129 |
| Hepatocyte ballooning score (0–2) | - | 1.7 (0.4) | 1.3 (0.3)* | 1.4 (0.5) | 1.0 (0.3)* | 0.314 |
| Steatosis score (0–3) | - | 2.2 (0.7) | 1.2 (0.7)† | 2.4(0.5) | 1.3(0.5)† | 0.485 |
| Lobular inflammation score (0–3) | - | 1.9 (0.6) | 1.6 (0.5)† | 1.5 (0.4) | 1.1 (0.4)† | 0.759 |
| Kleiner fibrosis stage (0–4) | - | 2.0 (1.3) | 1.9 (1.3) | 1.8 (0.9) | 1.7(0.8) | 0.748 |
| Kleiner fibrosis stages | ||||||
| F0 F1 F2 F3 F4 |
- | 1 (3%) 6 (19%) 15(47%) 9 (28%) 2 (6%) |
3 (9%) 11 (34%) 9(28%) 7 (22%) 3 (9%) |
1 (5%) 7 (35%) 6(30%) 4 (25%) 1 (5%) |
1 (5%) 6 (30%) 4(20%) 4 (20%) 2 (10%) |
0.397 0.447 0.693 0.559 0.692 |
| Clinically significant fibrosis (stage F2-4) n (%) | - | 26 (81%) ¶ | 19 (73%) | 11 (55%) | 10 (50%) | 0.394 |
| Advanced fibrosis (stage 3-4) n (%) | - | 11 (34%) | 10 (31%) | 5 (25%) | 6 (30%) | 0.893 |
| Biopsy length (mm) | - | 19.8 (5.1) | 19.5 (5.4) | 21.2 (4.6) | 20.9 (4.9) | 0.713 |
| Number of portal tracts | - | 19 (7) | 20(7) | 18 (6) | 18 (6) | 0.849 |
| Hepatic nuclear NF-kB (% positive cells) |
- | 51(2) | 27(2)‡ | 34(2) | 21(2)‡ | 0.218 |
| Standard Oral tolerance test |
||||||
| Fasting Tg (mg/dL) | 128 (27) | 151 (33) | 134 (35) | 143 (29) | 122 (24) | 0.285 |
| IAUC Tg (mg/dL x hr) | 115(17) | 197(21) | 148(17)* | 182(24) | 132(21)* | 0.297 |
| Fasting NEFA (mmol/L) | 0.31 (0.21) | 0.56 (0.24) | 0.46 (0.24) | 0.52 (0.21) | 0.41 (0.21) | 0.313 |
| IAUC NEFA (mMol/L x hr) | 0.92(0.38) | 1.97(0.43) | 1.11(0.38) | 1.81(0.48) | 1.03(0.33) | 0.339 |
| Fasting LDL-C (mg/dL) | 121 (16) | 138 (24) | 122 (18)* | 136 (26) | 119 (19)* | 0.461 |
| IAUC LDL-C (mg/dL x hr) | 38(21) | 43(22) | 32(18) | 48(24) | 36(22) | 0.398 |
| Fasting plasma glucose (mg/dL) | 104 (17) | 118 (28) | 110 (22) | 117 (22) | 109 (20) | 0.397 |
| Fasting GLP-1 (pmol/L) | 6.9(2.2) | 6.1(1.9) | 7.1(2.9) | 6.2(2.0) | 6.9(1.9) | 0.793 |
| IAUC GLP-1 (pmol/L x hr) | 17.4(3.9) | 12.2(3.4) | 17.1(3.2)† | 12.9(3.1) | 16.8(2.8)† | 0.395 |
| Fasting GLP-2 (ng/L) | 11.9(1.5) | 10.6(1.8) | 11.1(2.0) | 11.1(1.7) | 11.6(1.9) | 0.491 |
| IAUC GLP-2 (ng/L x hr) | 33.2(3.4) | 12.5(2.1)# | 29.1(2.7)† | 17.9(2.4) | 34.3(3.0)† | 0.296 |
| Fasting GIP (pg/mL) | 10.6(1.2) | 10.1(1.6) | 10.9(1.5) | 11.1(1.4) | 10.7(1.9) | 0.739 |
| IAUC GIP (pg/mL x hr) | 14.1(2.9) | 12.1(3.1) | 13.2(2,9) | 13.4(2.8) | 14.7(2.8) | 0.513 |
| fasting Zonulin (ng/mL) | 465(82) | 501(72) | 486(78) | 482(79) | 469(78) | 0.839 |
| IAUC Zonulin (ng/mL x hr) | 513(82) | 1093(102)¶ | 607(81) | 788(81) | 496(73) | 0.193 |
| Fasting plasma LPS (IU/mL) | 4.9(1.0) | 5.1(1.0) | 4.9(1.0) | 4.7(0.9) | 4.5(1.0) | 0.392 |
| IAUC plasma LPS (IU/mL x hr) |
9.7(1.4) | 47.2(3.9)# | 10.7(1.2)† | 29.9(3.4) | 9.3(1.8)† | 0.129 |
| Fasting FGF-21 (pg/mL) | 224(29) | 427(41) | 321(46) | 397(37) | 348(39) | 0.397 |
| IAUC FGF-21 (pg/mL x hr) | 174(37) | 38(19) | 168(39)† | 21(13) | 171(36)† | 0.491 |
| Fasting adiponectin (ng(mL) | 11032 ± 839§ | 6871 ± 610 | 7014 ± 570 | 6497 ± 404 | 6816 ± 611 | 0.779 |
| IAUC adiponectin (ng/mL x hr) |
9504 ± 918§ | -1947 ± 510 | 5417 ± 371† | -2039 ± 521 | 5917 ± 438† | |
| Fasting MCP-1 (pg/mL) | 163(28) | 193(32) | 179(24) | 181(28( | 175(22) | 0.312 |
| IAUC MCP-1 (pg/mL x hr) | 139(31) | 541(44) # | 271(31)‡ | 377(39) | 263(32)† | 0.485 |
| nuclear NF-κB in MNCs (%DNA binding activity) |
26(2) | 28(2) | 26(2) | 26(2) | 25(2) | 0.691 |
| IAUC nuclear NF-κB in MNCs (%DNA binding activity x hr) |
79(18) | 316(48) # | 76(13)‡ | 142(36) | 58(11)‡ | 0.198 |
Data are presented as n(%) or mean ± SEM.
Abbreviations: Tg: triglyceride; C: cholesterol; FGF: Fibroblast Growth Factor; MASH: Metabolic dysfunction-associated steatohepatitis; MCP: monocyte chemoattractant protein; MNCs: mononuclear cells: NF-κB: nuclear factor-κB; HOMA-IR=homeostasis model assessment of insulin resistance. DPP-IV: dipeptidyl peptidase (DPP) IV inhibitors; oxLDL: oxidized LDL; Tg: triglyceride.
*.p < 0.05 vs. baseline within the same group (MASH-CKD or MASH without CKD).
†.p < 0.01 vs. baseline within the same group.
‡.p < 0.001 vs. baseline within the same group.
¶p < 0.01 vs. MASH without CKD at baseline.
#p < 0.001 vs. MASH without CKD at baseline.
§p < 0.05 vs MASH.
ԥ p < 0.01 vs MASH.
Hyperlipidaemia was defined as recorded in the past medical history, as receiving lipid-lowering drugs (eg, statin, fibrate, ezetimibe), or both.
Hypertension was defi ned as recorded in the past medical history, as receiving an anti – hypertensive drug, or both. ‡LDL concentration was calculated using the Friedewald formula.
Non-alcoholic fatty liver disease (NAFLD) activity score is the algebraic sum of steatosis score, lobular inflammation score and hepatocyte ballooning score.
estimated Glomerular Filtration Rate (eGFR) was assessed from serum creatinine using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation, as recommended by KDIGO guidelines. eGFR and urinary albumin excretion rate (AER) were classified according to Kidney Disease Improving Global Outcomes (KDIGO) categories.
Chronic kidney disease (CKD) was defined according to KDIGO guidelines as sustained (i.e., documented on 2 occasions at least 3 months apart) reduction in eGFR <60 mL/min/1.73 m2 and/or sustained increase in AER ≥30 mg/d. CKD regression was defined as sustained (i.e., documented 3 month apart, on both follow-up visit 7 and visit 8) eGFR ≥60 mL/min/1.73 m2 and normalization of AER (i.e., <30 mg/d).
HOMA-IR=homeostasis model assessment-estimated insulin resistance.
NF-kB activation in circulating mononuclear cells (MNCs) drives their differentiation into an inflammatory phenotype, recruits them to target organs and has been implicated in experimental models of obesity/overnutrition-induced MASH and CKD and in human diabetic nephropathy19–21
Monocyte Chemoattractant Protein (MCP)-1, a downstream NF-kB target gene and a pivotal chemokine promoting monocyte recruitment to target organs in MASH and CKD (supplementary material).
We also included 26 age-, gender, BMI-, diabetes-matched controls without liver or kidney disease, who underwent the same assessment as MASH patients, including the meal tolerance test.
Details on sample size estimation, controls selection and analytics are provided in supplementary material.
The following outcomes were assessed:
CKD presence and regression, defined according to Kidney Disease Improving Global Outcomes (KDIGO) guidelines as sustained (i.e., documented 3 months apart, on both follow-up visit 7 and visit 8) eGFR ≥90 mL/min/1.73 m2 (as assessed by CKD-EPI equation) and normalization of albumin excretion rate (AER)(i.e., <30 mg/d)(details in supplementary material).
change in absolute values and in KDIGO categories of eGFR and AER.
All data were analyzed on an intention-to-treat basis.
Subjects with and without CKD at EOT were compared for baseline values and for changes during follow-up. Normality was evaluated by Shapiro-Wilk test and non-normal values were log-transformed for regression analysis. Fisher or chi square test were used to compare categorical variables, as appropriate. Differences across groups were analyzed by ANOVA and then by Bonferroni correction, when variables were normally distributed; otherwise the Kruskal-Wallis test, followed by the post hoc Dunn test, was used to compare nonparametric variables.
To adjust for multiple comparison testing, the Benjamini-Hochberg False Discovery Rate correction was applied to raw p-values in all comparisons; significance was set at an adjusted p-value threshold of 0.05.
For the meal tolerance test, the area under the curve (AUC) and incremental AUC (IAUC) of parameters were computed by the trapezoid method and compared with ANOVA repeated measures.
Univariate and subsequent multivariate regression analyses were used to identify predictors of CKD at baseline and of CKD absence at EOT in MASH patients.
Adjustments was made for treatment allocation and for variables a priori known to be associated with CKD progression (age, baseline eGFR, baseline urinary AER, diabetes, baseline hepatic fibrosis stage (F0–2 vs F34), systolic blood pressure, LDL-cholesterol, HbA1c change, angiotensin-converting enzyme/angiotensin receptor blocker/statin use. Additional covariates were selected from parameters which differed at baseline between MASH patients with CKD and MASH without CKD. Continuous outcome measures were compared using linear regression, adjusting for parameter baseline values and allocated treatment (as model covariates, equivalent to ANCOVA), with multilevel modeling for key continuous outcome measures to account for repeated measures within each patient.
Use of GLP-1 analogues or SGLT2 inhibitors was an exclusion criterion from the trial.
Results
At baseline, 32 (61%) out of 52 MASH patients had CKD and 20 (39%) had normal renal function: despite similar fasting biochemical, genetic, clinical and metabolic profile, after the meal challenge MASH patients with CKD showed impaired GLP-2 response, increased intestinal permeability and endotoxemia, and higher NF-kB activation in circulating MNCs than MASH patients without CKD, with LPS and NF-kB activation in MNCs peaking 2 hours after GLP-2 (Table 1, Figure 1a–c).
Figure 1a.
Panel A: GLP-2 response during the standardized meal tolerance test. Patients (n = 52) were grouped according to the presence (n = 32) or absence (n = 20) of chronic kidney disease(ckd) at baseline. Panel B: plasma lipopolysaccharide(lps) during the standardized meal tolerance test. Patients (n = 52) were grouped according to the presence (n = 32) or absence (n = 20) of chronic kidney disease(ckd) at baseline. Panel C: nf-kB activation in circulating mononuclear cells (MNCs) during the standardized meal tolerance test according to the presence (n = 32) or absence (n = 20) of chronic kidney disease(ckd) at baseline. Panel D: GLP-2 response during the standardized meal tolerance test at baseline. Patients (n = 52) were grouped according to liver disease activity, quantified as NAFLD activity Score(NAS). NAS is the algebraic sum of steatosis score (range 0-3), lobular inflammation (range 0-3) score and hepatocyte ballooning score (range 0-2). A NAS > 4 indicates high disease activity. Panel E: GLP-2 response during the standardized meal tolerance test at baseline. Patients (n = 52) were grouped according to fibrosis score (range 0-4). A fibrosis score ≥ 2 indicates clinically significant fibrosis. Panel F: GLP-2 response during the standardized meal tolerance test. Patients (n = 52) were grouped according to treatment allocation (phospholipid curcumin meriva or placebo). Panel G: changes (%) in nf-kB activation in circulating mononuclear cells (MNCs) during the standardized meal tolerance test. Patients (n = 52) were grouped according to treatment allocation (phospholipid curcumin meriva or placebo). Panel H: correlation between changes in postprandial GLP-2 response and changes in CKD status (present/absent) during the treatment in the whole NASH s population (n = 52). Panel I: correlation between changes in postprandial GLP-2 response and changes in absolute values of eGFR during the treatment in the whole NASH s population, grouped according to treatment arm (n = 52).
Figure 1b.
Continue
Consistent with existing literature3,4 NASH patients with CKD tended to have higher baseline histological NAFLD activity score (NAS) and a higher prevalence of clinically significant (stage F2–4) fibrosis than patients without CKD (Table 1). However, when grouping MASH patients according to the severity of histological disease activity (NAS ≤4 vs. NAS > 4) and of fibrosis (Fibrosis stage F0–1 vs. stage ≥F2), MASH patients with CKD showed an impaired postprandial GLP-2 response compared with patients without CKD (Figure 1d–e). Consistently, on multivariate regression analysis postprandial GLP-2 response was inversely associated with CKD independently of histological liver disease severity and of traditional CKD risk factors (Table 2 panel A),
Table 2.
Logistic regression analysis in the whole MASH population(n = 52) for the presence of CKD at baseline and at end-of-treatment (EOT).
| Panel A Outcome: presence of CKD at baseline | ||
|---|---|---|
| Model | Odds ratio (95% CI) | p-value |
| Baseline hepatic fibrosis stage F2-4 | 1.79 (0.81-8.02) | 0.106 |
| Baseline NAFLD Activity Score (NAS) | 1.48 (0.78-7.85) | 0.382 |
| Diabetes (present) | 0.89 (0.48-4.32) | 0.729 |
| Obesity (present) | 0.68 (0.10-6.79) | 0.872 |
| Dyslipidemia (present) | 1.12 (0.39-12.73) | 0.471 |
| Hypertension (present) | 0.52(0.14-4.91) | 0.493 |
| ACEIs/ARBs use | 0.99 (0.80-4.37) | 0.871 |
| IAUC GLP-2 (highest tertile) | 0.43 (0.32-0.80) | 0.008 |
| IAUC plasma LPS (highest tertile) | 3.42 (0.89-7.75) | 0.113 |
| IAUC serum Zonulin (highest tertile) | 1.94(0.39-3.71) | 0.496 |
| IAUC NF-kB activation in circulating MNCs (highest tertile) | 3.29 (0.87-6.35) | 0.109 |
| IAUC plasma MCP-1 (highest tertile) | 1.56 (0.39-3.18) | 0.778 |
| Panel B Outcome: CKD at EOT | ||
| Model | Odds ratio (95% CI) | p-value |
| Age | 0.99 (0.80-4.37) | 0.871 |
| Treatment allocation (curcumin) | 4.99 (0.81-19.02) | 0.112 |
| HbA1c change | 0.61 (0.18, 2.59) | 0.692 |
| eGFR at baseline | 0.48 (0.09-4.83) | 0.837 |
| AER at baseline | 0.26 (0.07-4.92) | 0.328 |
| Systolic BP change | 0.73 (0.14-3.97) | 0.512 |
| LDL-cholesterol change | 1.01 (0.39-4.26) | 0.720 |
| IAUC GLP-2 change | 0.49 (0.21-0.73) | 0.010 |
| IAUC serum Zonulin change | 0.98(0.24-4.94) | 0.458 |
| IAUC plasma LPS change | 2.30(0.39-3.19) | 0.315 |
| IAUC NF-kB activation in circulating MNCs change(%) | 3.31 (0.94-6.72) | 0.111 |
| IAUC plasma MCP-1 change | 3.98(0.72-3.81) | 0.112 |
| Panel C Outcome: eGFR change at EOT | ||
| Model | β (SE) | p-value |
| Age | -0.199 (0.127) | 0.210 |
| Treatment allocation (curcumin) | 0.231(0.138) | 0.227 |
| HbA1c change | 0.214(0.192) | 0.613 |
| eGFR at baseline | -0.332(0.003) | 0.025 |
| AER at baseline | -0.201(0.172) | 0.308 |
| Systolic BP change | -0.199(0.127) | 0.210 |
| LDL-cholesterol change | -0.288(0.139) | 0.407 |
| IAUC GLP-2 change | 0.510(0.007) | 0.006 |
| IAUC serum Zonulin change | -0.284(0.009) | 0.219 |
| IAUC plasma LPS change | -0.391(0.198) | 0.128 |
| IAUC NF-kB activation in circulating MNCs change(%) | -0.310(0.012) | 0.168 |
| IAUC plasma MCP-1 change | -0.313(0.007) | 0.068 |
All analyses will be carried out with Easy R ver1.61, Saitama, Japan.
1Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013;48:452-8.
Collectively, these findings point to an impaired postprandial GLP-2 response as a distinct feature of CKD in MASH.
Meriva treatment was associated with a significant increase in postprandial GLP-2 response, coupled with an improvement in postprandial intestinal permeability, endotoxemia and NF-kB activation in circulating MNCs (Table 1, Figure 1f–g).
At EOT, 20 (63%) of patients with baseline CKD showed regression of renal disease, while 4(20%) patients without CKD at baseline developed CKD (Table 1).
At follow-up data analysis, the changes in postprandial GLP-2 response significantly predicted the presence of CKD and eGFR changes at EOT on univariate and multivariate regression analyses, with the slope of correlation between changes in IAUC GLP-2 and in eGFR overlapping in the two treatment arms (Figure 1h–i, Table 2).
Conclusion
In this study we scrutinized the major pathways involved in postprandial nutrient handling in biopsy-proven MASH patients with or without CKD: our findings disclosed for the first time an altered postprandial GLP-2/LPS/NF-kB pathway response as a distinct pathogenic feature of renal disease in MASH. Consistent with existing data,4 MASH patients with CKD tended to have more severe liver histology than MASH patients with normal renal function at baseline : by using univariate and multivariate analyses (Figure 1d–e, Table 2A) we dissected the effect of GLP-2 response to nutrients from that of baseline liver disease severity and of traditional risk factors for CKD and showed that impaired postprandial GLP-2 response was a distinct feature of renal disease in MASH. Baseline cross-sectional association between GLP-2 and CKD was confirmed longitudinally by exploring GLP-2/LPS/NF-kB pathway changes following treatment: in univariate analysis, the slope of correlation between changes in IAUC GLP-2 and in eGFR was statistically significant and similar between the two study arms, indicating a tight relationship between the two variables regardless of the type of treatment; more importantly, such relationship was confirmed in multivariate analyses, where changes in IAUC GLP-2 predicted eGFR changes and presence of CKD at EOT independently of treatment allocation, liver disease severity and known risk factors for CKD progression (Figure 1 panel I, Table 2). Collectively, these data suggest that CKD may signal dysfunctional GLP-2 response to meals in MASH, with repetitive, meal-associated bouts of endotoxemia and pro-inflammatory NF-kB activation in MNCs potentially injuring the kidney.
Thorough nutritional and metabolic assessment of MASH patients, who were also characterized in the postprandial phase, is a major strength of this report; the small sample size and the selected MASH population from two gastroenterological tertiary referral centers may however limit generalizability of our findings, which warrant confirmation in a larger unselected MASH population with the whole spectrum of CKD; furthermore, the relatively short duration of the follow-up, which was chosen following regulatory agencies’ recommendations for MASH drug development22,23prevented evaluation of hard liver-related and renal clinical outcomes.
If confirmed, the insights from this report may have therapeutically relevant implications: while previous reports suggested altered gut microbiota may contribute to CKD,24 identifying a specific downstream molecular pathway linking gut dysbiosis to systemic inflammation may be a more effective therapeutic strategy than gut microbiota manipulation with pro- or pre-biotics. There are several druggable targets in the GLP-2/LPS/NF-kB axis that are still unexplored in MASH,25 most prominently GLP-2 response. GLP-2 is an prominent regulator of intestinal permeability which is secreted by enteroendocrine L cells following meals and enhances intestinal barrier integrity: consistently, GLP-2 analogues26 are currently used in short bowel syndrome and may represent an attractive therapeutic tool in MASH by their anti-inflammatory effects, which are both indirect, mediated through amelioration of intestinal permeability and metabolic endotoxemia, and direct, by inhibition of NF-kB activation by LPS and NEFAs in MNCs.7,26,27
Supplementary Material
Funding Statement
This study was funded partly by Indena S.p.A., Milan, Italy, and partly by a grant to RG for the Italian Ministry of University and Research, (MIUR, Rilo, ex 60%, 2023). The sponsor provided funds to acquire kits for biochemical and histological assessments; it had no role in study design, data acquisition and analysis and discussion.
Disclosure statement
Silvia Pinach: no present or past financial or non-financial competing interest to disclose
Mella Alberto: no present or past financial or non-financial competing interest to disclose
Filippo Mariano: no present or past financial or non-financial competing interest to disclose
Maurizio Cassader: no present or past financial or non-financial competing interest to disclose
Franco De Michieli: no present or past financial or non-financial competing interest to disclose
Antonella Riva: no present or past financial or non-financial competing interest to disclose
Giovanna Petrangolini: no present or past financial or non-financial competing interest to disclose
Stefano Togni: no present or past financial or non-financial competing interest to disclose
Roberto Gambino: no present or past financial or non-financial competing interest to disclose
Author contribution
Giovanni Musso: designed the trial, acquired and analyzed data, drafted the work, approved the final version, agreed to be accountable for all aspects of the work. He is the guarantor of the article.
Silvia Pinach: gave substantial contributions to the study design and data acquisition, revised the work critically for important intellectual content; approved the final version to be published; agreed to be accountable for all aspects of the work.
Mella Alberto: gave substantial contributions to the study design and data acquisition, revised the work critically for important intellectual content; approved the final version to be published; agreed to be accountable for all aspects of the work.
Filippo Mariano: gave substantial contributions to the study design and data acquisition, revised the work critically for important intellectual content; approved the final version to be published; agreed to be accountable for all aspects of the work
Maurizio Cassader: gave substantial contributions to the study design and data acquisition, revised the work critically for important intellectual content; approved the final version to be published; agreed to be accountable for all aspects of the work
Franco De Michieli: gave substantial contributions to the study design and data acquisition, revised the work critically for important intellectual content; approved the final version to be published; agreed to be accountable for all aspects of the work
Antonella Riva: gave substantial contributions to the study design and data acquisition, revised the work critically for important intellectual content; approved the final version to be published; agreed to be accountable for all aspects of the work
Giovanna Petrangolini: gave substantial contributions to the study design and data acquisition, revised the work critically for important intellectual content; approved the final version to be published; agreed to be accountable for all aspects of the work
Stefano Togni: gave substantial contributions to the study design and data acquisition, revised the work critically for important intellectual content; approved the final version to be published; agreed to be accountable for all aspects of the work
Roberto Gambino: gave substantial contributions to the study design and data acquisition, revised the work critically for important intellectual content; approved the final version to be published; agreed to be accountable for all aspects of the work
Data sharing statement
Deidentified participant data will be made available with publication to anyone upon reasonable request to corresponding author.
Supplementary Material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2024.2424907
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