Thaninee Prasoppokakorn, M.D.1,2, Varisa Limpijankit2, Siravich Seesodsai, M.D.2, Palakorn Panarat, M.D.2, Teerarat Shanthachol, M.D.2, Kanokwan Sonsiri1, Prooksa Ananchuensook, M.D.1, Kessarin Thanapirom, Ph.D.1, Chusana Suankratay, M.D., Ph.D.3, Sombat Treeprasertsuk, M.D., Ph.D.1,*
1Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital,
Thai Red Cross Society, Bangkok, Thailand, 2Department of Medicine, Queen Savang Vadhana Memorial Hospital, Chonburi, Thailand, 3Division of Infectious disease, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand.
*Corresponding author: Sombat Treeprasertsuk E-mail: battan5410@gmail.com
Received 23 August 2024 Revised 29 September 2024 Accepted 30 September 2024 ORCID ID:http://orcid.org/0000-0001-6459-8329 https://doi.org/10.33192/smj.v76i12.270742
All material is licensed under terms of the Creative Commons Attribution 4.0 International (CC-BY-NC-ND 4.0) license unless otherwise stated.
ABSTRACT
INTRODUCTION
People living with HIV (PLWH) in Thailand nowadays experience extended lifespans comparable to those of the general population due to advances in antiretroviral therapy (ART).1,2 However, the prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) in PLWH is rising globally, with estimates ranging between 30-50%.3-5 This association is attributed to factors such as an aging population, a high prevalence of metabolic risk factors, comorbidities, and HIV-related infection.6-9 Additionally, ART has been associated with negative metabolic side effects, such as weight gain and hepatic steatosis, further elevating the risk of MASLD.10-12
MASLD in PLWH is concerning due to its association with advanced fibrosis, cirrhosis, hepatocellular carcinoma, and reduced survival.13-15 Early detection of MASLD can inform clinical management, allowing for lifestyle modification or therapeutic intervention to mitigate potential complications. While the reference standard for assessing prognosis and disease monitoring involves the histologic examination of liver biopsy specimens, this invasive procedure may not be the preferred approach in patients with HIV and MASLD. In such case, the utilization of non-invasive scoring systems is valuable for facilitating risk stratification and longitudinal assessment of disease progression.16-18
Several non-invasive scoring systems, including the fibrosis-4 (FIB-4) score, aspartate aminotransferase to platelet ratio index (APRI), and non-alcoholic fatty liver disease (NAFLD) fibrosis score (NFS), are commonly used for detecting liver fibrosis in this population.19 These scoring systems are generally safer and more acceptable for patients, making them well-suited for serial monitoring of disease progression or response to treatment.
The identification of advanced liver fibrosis serves as a crucial prognostic marker for unfavorable outcomes in individuals with MASLD. Currently, imaging-based biomarkers play a significant role, offering rapid and outpatient-compatible procedures. Among the ultrasound- based techniques, vibration-controlled transient elastography (VCTE), commercially available as FibroScan® (Echosens, Paris, France), was the first to be introduced and is currently the most extensively validated.20-21 This non- invasive imaging technique provides a dual advantage for screening, offering simultaneously information on both liver stiffness measurement (LSM) and the degree of hepatic steatosis, estimated through the controlled attenuation parameter (CAP).
It is important to note that the diagnostic performance of these tests can vary based on the specific population studies, the prevalence of liver fibrosis in that population,
and the reference standard used for comparison.22-24 The diverse influences of ethnicity, race, socioeconomic status, and varying lifestyles contribute to the impact of liver fibrosis in people living with HIV. However, there is limited data about the importance of liver fibrosis and fatty liver in PLWH, particularly among Asian population.25-26 Therefore, this study aims to identify clinical predictors of significant fibrosis in PLWH with MASLD in Thailand.
MATERIALS AND METHODS
We conducted a retrospective cohort study, enrolling patients from two tertiary hospitals between April and October 2023. The inclusion criteria were patients aged
≥18 years who visited outpatient liver and infectious disease clinics with HIV infection, had been treated with standard antiretroviral therapy for ≥6 months, and had an HIV RNA count ≤200 copies/mL. Our study included patients with hepatitis B and C coinfections, as well as those who consumed alcohol. There were no exclusion criteria. Baseline patient characteristics such as age, gender, body weight, body mass index (BMI), waist and hip circumference, and underlying comorbidities including type 2 diabetes mellitus, dyslipidemia, and hypertension were recorded. Personal history information on smoking and alcohol consumption was also retrieved. Laboratory tests included liver function tests, metabolic profiles (fasting blood sugar, HbA1C, lipid profile), CD4 counts, and HBV and HCV status. The laboratory tests were conducted within six months of the hepatic steatosis and fibrosis assessment.
Steatosis and fibrosis assessment: All PLWH underwent a FibroScan® study conducted by dedicated operators after fasting for less than 3 hours. Two parameters derived from this vibration-controlled transient elastography (VCTE) were the controlled attenuation parameter (CAP) and liver stiffness measurement (LSM), which reflected steatosis (in decibels per meter or dB/m) and fibrosis (in kilopascals or kPa), respectively. The cut-off values to define steatosis were: no signs of steatosis (S0) < 238 dB/m, beginning steatosis (S1): 238-259 dB/m, advanced steatosis (S2): 260-291 dB/m, and severe steatosis (S3) > 292 dB/m. A TE cut-off value of at least 6.5 kPa indicated liver fibrosis (F1), and 8.0 kPa indicated significant fibrosis (F2). For more advanced stages, a TE value of ≥9.5 kPa was used to define advanced fibrosis (F3), while a TE value of ≥12.5 kPa indicated cirrhosis (F4).28
Fibrosis scoring systems were also calculated. The fibrosis-4 score (FIB-4 score) was determined using the formula [Age (years)×AST (U/L)/[platelet
(109/L)×ALT1/2 (U/L)],29 the AST to platelet ratio index (APRI) was computed as [AST (IU/L)/AST upper limit of normal (IU/L)/platelets (109/L) x 100],30 and the NAFLD fibrosis score (NFS) was derived from the formula [-1.675
+ 0.037 × age (years) + 0.094 × BMI (kg/m2) + 1.13 × diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio - 0.013
× platelet count (x109/L) - 0.66 × albumin (g/dL).31 Individual consent for retrospective analysis was
exempted. The study protocol was approved by the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University (IRB No.903/63), and adhered to the principles outlined in the Helsinki Declaration of 1983.
According to a recently published international consensus statement,32 a diagnosis of MASLD required the presence of hepatic steatosis, as defined by a CAP of ≥ 238 dB/m. Apart from the identification of hepatic steatosis detected by imaging or biopsy, at least one of the following five criteria had to be evident: 1) overweight/ obesity (BMI > 23 kg/m2) or waist circumference > 90 cm in men, > 80 cm in women, 2) fasting serum glucose levels > 100 mg/dL or 2-hour post-load glucose level > 140 mg/dL or HbA1c > 5.7%, or specific drug treatment,
3) blood pressure > 130/85 mmHg or specific drug treatment, 4) plasma TG levels > 150 mg/dL or specific drug treatment, 5) plasma HDL-C levels below 40 mg/ dL for men and 50 mg/dL for women or specific drug treatment. Dyslipidemia was defined as elevated levels of serum TC, LDL-C, TG, or a reduced serum HDL-C concentration.
The primary outcome was identifying clinical predictors of significant fibrosis (F ≥2), defined as TE
≥ 8 kPa in PLWH with MASLD. Secondary outcomes included: 1) the prevalence and clinical characteristics of MASLD, defined as CAP ≥ 238 dB/m in those meeting cardiometabolic criteria; 2) the performance of non-invasive fibrosis scoring systems among these patients; and 3) the clinical applicability of the two-step non-invasive testing recommended by the American Gastroenterological Association (AGA) pathway, using the FIB-4 score and VCTE.
Continuous variables were reported as mean ± standard deviation and analyzed using unpaired t-tests for normally distributed data. Skewed variables were expressed as median with interquartile range and assessed
for differences using the Mann-Whitney U test. Categorical variables were expressed as numbers (percentages) and compared using Fisher’s exact test or Chi-square test as appropriate. Factors associated with significant fibrosis were determined using a logistic regression model. Age, sex, and other factors with a p-value of < 0.05 in the univariate model were included in the multivariate model. Statistical analyses were performed using SPSS version
22.0.0 (SPSS Inc., Chicago, Illinois, USA). A p-value of
< 0.05 was considered statistically significant.
The study protocol received approval from the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University (IRB number 903/63).
RESULTS
A total of 96 participants with HIV infections were enrolled at two tertiary hospitals. Of these, 52 (54.2%) were diagnosed with MASLD. Table 1 demonstrates the baseline characteristics between the MASLD and non- MASLD groups. Patients with MASLD had significantly higher body weight (70.9 ± 11.9 kg vs. 61.1 ± 8.8 kg, p
< 0.001), BMI (25.8 ± 4.1 kg/m² vs. 21.6 ± 3.0 kg/m², p
< 0.001), waist circumference (WC) (35.8 ± 3.5 inches vs. 31.4 ± 3.2 inches, p < 0.001), and hip circumference (HC) (39.4 ± 3.6 inches vs. 35.7 ± 3.2 inches, p < 0.001) compared to those without MASLD. Additionally, 100% of the MASLD group had a BMI ≥30, while none of the non-MASLD group did (p < 0.001). Comorbidities such as impaired fasting glucose (IFG), diabetes (DM), dyslipidemia (DLP), and hypertension (HT) showed a trend towards higher incidence in the MASLD group compared to the non-MASLD group, although these differences did not reach statistical significance (IFG: 44.2% vs. 27.3%, p = 0.083; DM: 19.2% vs. 6.8%, p =
0.068; DLP: 46.2% vs. 36.4%, p = 0.331; HT: 34.6% vs.
20.5%, p = 0.121).
Regarding laboratory values, fasting blood sugar (FBS) was significantly higher in the MASLD group compared to the non-MASLD group (103.8 ± 25.5 mg/ dL vs. 96.5 ± 9.4 mg/dL, p = 0.029). Triglyceride (TG) levels were also significantly higher in the MASLD group (158.7 ± 82.9 mg/dL vs. 121.3 ± 72.6 mg/dL, p = 0.010), and LDL cholesterol was significantly elevated in the MASLD group as well (118.8 ± 36.7 mg/dL vs. 107.0 ± 28.9 mg/dL, p = 0.041). However, there were no significant differences in AST, ALT, or HDL cholesterol between the two groups. Liver stiffness, as measured by transient elastography (TE), was significantly higher in
the MASLD group (8.7 ± 7.8 kPa vs. 7.5 ± 8.6 kPa, p = 0.014). Additionally, the CAP score, indicating hepatic steatosis, was also significantly higher in the MASLD group (285 ± 36 dB/m vs. 195 ± 28 dB/m, p < 0.001).
Among the MASLD patients, the mean age was
49.7 ± 8.0 years, and 33 (63.5%) were male. The mean BMI was 25.8 ± 4.1 kg/m². Obesity, defined by WHO criteria for the Asia-Pacific region (BMI ≥ 30.0 kg/m²), was present in 9 (17.3%) patients. Dyslipidemia was the most common comorbidity, affecting 24 (46.2%) patients, followed by diabetes in 10 (19.2%) patients and hypertension in 18 (34.6%) patients. Regarding hepatitis virus coinfection, 5 (9.6%) patients had HBV and 7 (13.5%) had HCV. Additionally, two-thirds of the patients (67.3%) had a history of alcohol consumption. Baseline characteristics of the total cohort are shown in Table 2.
Regarding steatosis, the mean CAP was 285 ± 36 dB/m, and the median CAP was 276 (255, 303) dB/m. Among the patients, 16 (30.8%) had steatosis grade 1,
17 (32.7%) had grade 2, and 19 (36.5%) had grade 3. For liver fibrosis, the mean TE was 8.7 ± 7.8 kPa, and the median TE was 6.1 (5.2, 8.8) kPa. Significant fibrosis (F ≥2) was found in 24 patients (46.2%), advanced fibrosis (F ≥3) in 8 patients (15.4%), and cirrhosis (F ≥4) in 4 patients (7.7%).
Patients with significant fibrosis (≥ F2) compared to those with fibrosis stage 0-1 had significantly higher BMI (27.1 ± 4.6 vs 24.7 ± 3.3 kg/m², p = 0.016), higher
waist circumference (37.1 ± 3.4 vs 34.6 ± 3.3 inches, p = 0.004), and higher hip circumference (40.7 ± 3.7 vs 38.3 ± 3.2 inches, p = 0.008). These patients also had a higher prevalence of dyslipidemia (62.5% vs 32.1%, p = 0.029) and HCV co-infection (29.2% vs 0%, p = 0.002). Additionally, they exhibited higher AST levels (53.5 vs 28.2 U/L, p < 0.001), ALT levels (72.1 vs 37.9 U/L, p
= 0.002), and globulin levels (3.5 ± 0.6 vs 3.3 ± 0.3 g/ dL, p = 0.035). Furthermore, they had a lower platelet count (221 ± 88 vs 290 ± 84, p = 0.003) and lower HDL-
cholesterol (41.4 ± 11.4 vs 48.3 ± 8.6 mg/dL, p = 0.008) (Table 2). However, no association was found between CAP grades and the degrees of liver fibrosis (p = 0.893).
Patients with significant fibrosis had significantly higher levels of all non-invasive fibrosis tests, including the FIB-4 score (2.54 ± 3.89 vs 0.86 ± 0.32, p < 0.001), APRI
TABLE 1. Baseline characteristics of the entire cohort compared between MASLD and non- MASLD (n=96).
Variables | Total (n=96) | MASLD (n=52) | Non-MASLD (n=44) | p-value |
Age (yr), mean ±SD | 48.9±9.9 | 49.7±8.0 | 48.1±11.8 | 0.213 |
Sex Male, n(%) | 68 (70.8%) | 33(63.5%) | 35 (79.5%) | 0.081 |
BW (kg) | 66.4±11.6 | 70.9±11.9 | 61.1±8.8 | <0.001 |
BMI (kg/m2) | 23.9±4.2 | 25.8±4.1 | 21.6±3.0 | <0.001 |
BMI ≥30, n(%) | 9 (9.4%) | 9 (100%) | 0 (0%) | <0.001 |
WC (inch) | 33.8±4.0 | 35.8±3.5 | 31.4±3.2 | <0.001 |
HC (inch) | 37.7±3.8 | 39.4±3.6 | 35.7±3.0 | <0.001 |
IFG, n(%) | 35 (36.5%) | 23 (44.2%) | 12 (27.3%) | 0.083 |
T2DM, n(%) | 13 (13.5%) | 10 (19.2%) | 3 (6.8%) | 0.068 |
DLP, n(%) | 40 (41.7%) | 24 (46.2%) | 16 (36.4%) | 0.331 |
HT, n(%) | 27 (28.1%) | 18 (34.6%) | 9 (20.5%) | 0.121 |
HBV, n(%) | 6 (6.3%) | 5 (9.6%) | 1 (2.3%) | 0.120 |
HCV, n(%) | 18 (18.8%) | 7 (13.5%) | 11 (25%) | 0.149 |
Alcohol, n(%) | 69 (71.9%) | 35 (67.3%) | 34 (77.3%) | 0.521 |
Smoking, n(%) | 37 (38.5%) | 22 (42.3%) | 15 (34.1%) | 0.585 |
AST (U/L) | 38.9±23.5 | 39.9±25.4 | 37.8±21.3 | 0.329 |
ALT (U/L) | 49.6±42.5 | 53.7±43.2 | 44.8±41.7 | 0.155 |
ALP (U/L) | 87.7±30.3 | 89.1±26.0 | 86.1±34.9 | 0.634 |
Albumin (g/dL) | 4.3±0.4 | 4.3±0.3 | 4.2±0.4 | 0.638 |
Globulin (g/dL) | 3.4±0.5 | 3.4±0.5 | 3.4±0.5 | 0.788 |
Hb (g/dL) | 14.9±13.1 | 13.3±1.9 | 16.8±19.3 | 0.246 |
WBC (cell/µL) | 6829±1957 | 7087±2264 | 6525±1487 | 0.149 |
Platelet (x109/µL) | 251±85 | 258±92 | 243±77 | 0.387 |
Creatinine (mg/dL) | 1.0±0.3 | 1.0±0.2 | 1.0±0.3 | 0.867 |
FBS (mg/dL) | 100.5±20.1 | 103.8±25.5 | 96.5±9.4 | 0.029 |
TC (mg/dL) | 183.0±37.8 | 188.9±40.9 | 176.0±32.9 | 0.045 |
TG (mg/dL) | 141.5±80.2 | 158.7±82.9 | 121.3±72.6 | 0.010 |
HDL-chol (mg/dL) | 45.7±10.4 | 45.1±10.5 | 46.3±10.3 | 0.581 |
LDL-chol (mg/dL) | 113.4±33.7 | 118.8±36.7 | 107.0±28.9 | 0.041 |
CD4 (cell count) | 640±269 | 679±269 | 595±264 | 0.125 |
ARV used, n(%) | 96 (100%) | 52 (100%) | 44 (100%) | NA |
CAP (dB/m) Mean ±SD | 244±55 | 285±36 | 195±28 | <0.001 |
Median (IQR) | 246 (200,285) | |||
TE (kPa) Mean ±SD | 8.1±8.2 | 8.7±7.8 | 7.5±8.6 | 0.014 |
Median (IQR) | 5.8 (4.5,8.7) |
TABLE 2. Baseline characteristics compared between Fibrosis stage 0-1 and Fibrosis stage ≥2 of MASLD in PWLH (n=52).
Variables | Total (n=52) | Fibrosis stage F0-1 | Fibrosis stage ≥2 | p-value |
(n=28) | (n=24) | |||
Age (yr), mean ±SD | 49.7±8.0 | 48.3±9.1 | 51.3±6.4 | 0.085 |
Male, n (%) | 33 (63.5%) | 17 (60.7%) | 16 (66.7%) | 0.775 |
BW (kg) | 70.9±11.9 | 68.4±9.7 | 73.7±13.8 | 0.058 |
BMI (kg/m2) | 25.8±4.1 | 24.7±3.3 | 27.1±4.6 | 0.016 |
BMI ≥30, n (%) | 9 (17.3%) | 3 (10.7%) | 6 (25.0%) | 0.175 |
WC (inch) | 35.8±3.5 | 34.6±3.3 | 37.1±3.4 | 0.004 |
HC (inch) | 39.4±3.6 | 38.3±3.2 | 40.7±3.7 | 0.008 |
IFG, n (%) | 23 (44.2%) | 12 (42.9%) | 11 (45.8%) | 0.829 |
T2DM, n (%) | 10 (19.2%) | 7 (25.0%) | 3 (12.5%) | 0.254 |
DLP, n (%) | 24 (46.2%) | 9 (32.1%) | 15 (62.5%) | 0.029 |
HT, n (%) | 18 (34.6%) | 8 (28.6%) | 10 (41.7%) | 0.322 |
HBV, n (%) | 5 (9.6%) | 4 (14.3%) | 1 (4.2%) | 0.217 |
HCV, n (%) | 7 (13.5%) | 0 (0.0%) | 7 (29.2%) | 0.002 |
Alcohol, n (%) | 35 (67.3%) | 19 (67.9%) | 16 (66.7%) | 0.838 |
Smoking, n (%) | 22 (42.3%) | 15 (53.6%) | 7 (29.2%) | 0.184 |
Laboratory tests | ||||
AST (U/L) | 39.9±25.4 | 28.2±11.4 | 53.5±6.2 | <0.001 |
ALT (U/L) | 53.7±43.2 | 37.9±27.8 | 72.1±50.7 | 0.002 |
ALP (U/L) | 89.1±26.0 | 88.6±25.1 | 89.7±27.6 | 0.445 |
Albumin (g/dL) | 4.3±0.3 | 4.3±0.2 | 4.3±0.4 | 0.387 |
Globulin (g/dL) | 3.4±0.4 | 3.3±0.3 | 3.5±0.6 | 0.035 |
Hb (g/dL) | 13.3±1.9 | 13.6±2.1 | 13.1±1.6 | 0.152 |
WBC (cell/µL) | 7087±2264 | 7496±2243 | 6608±2239 | 0.080 |
Platelet (x109/µL) | 258±92 | 290±84 | 221±88 | 0.003 |
Creatinine (mg/dL) | 1.02±0.24 | 1.0±0.2 | 1.1±0.3 | 0.056 |
FBS (mg/dL) | 103.8±25.5 | 102.7±21.6 | 105.1±29.8 | 0.376 |
TC (mg/dL) | 188.9±40.9 | 193.9±43.1 | 183.0±38.3 | 0.171 |
TG (mg/dL) | 158.7±82.9 | 151.7±87.4 | 166.9±78.3 | 0.257 |
HDL-chol (mg/dL) | 45.1±10.5 | 48.3±8.6 | 41.4±11.4 | 0.008 |
LDL-chol (mg/dL) | 118.8±36.7 | 123.2±38.3 | 113.6±34.9 | 0.177 |
TABLE 2. Baseline characteristics compared between Fibrosis stage 0-1 and Fibrosis stage ≥2 of MASLD in PWLH (n=52). (Continue)
Variables | Total (n=52) | Fibrosis stage F0-1 (n=28) | Fibrosis stage ≥2 (n=24) | p-value |
HIV parameters | ||||
CD4 (cell count) | 679±269 | 697±265 | 658±279 | 0.304 |
ARV used, n (%) | 52 (100%) | 28 (100%) | 24 (100%) | NA |
FibroScan® study | ||||
CAP grade, n (%) 1 | 16 (30.8%) | 8 (28.6%) | 8 (33.3%) | 0.893 |
2 | 17 (32.7%) | 9 (32.1%) | 8 (33.3%) | |
3 | 19 (36.5%) | 11 (39.3%) | 8 (33.3%) | |
CAP (dB/m) Mean ±SD | 285±36 | 291.9±41.5 | 276.5±25.5 | 0.054 |
Median (IQR) | 276 (255,303) | |||
TE (kPa) Mean ±SD | 8.7±7.8 | 5.1±0.8 | 12.8±10.1 | <0.001 |
Median (IQR) | 6.1 (5.2,8.8) | |||
Non-invasive fibrosis testing | ||||
FIB-4 score Mean ±SD | 1.64±2.75 | 0.86±0.32 | 2.54±3.89 | <0.001 |
Median (IQR) | 1.06 (0.71,1.58) | |||
APRI score Mean ±SD | 0.54±0.68 | 0.27±0.15 | 0.87±0.89 | <0.001 |
Median (IQR) | 0.32 (0.20,0.63) | |||
NFS score Mean ±SD | -2.17±1.63 | -2.78±1.24 | -1.46±1.76 | 0.001 |
Median (IQR) | -2.30 (-3.30,-1.41) | |||
score (0.87 ± 0.89 vs 0.27 ± 0.15, p < 0.001), and NFS score
(-1.46 ± 1.76 vs -2.78 ± 1.24, p = 0.001), compared to those with fibrosis stage 0-1. Using transient elastography as the reference diagnostic test, we assessed the efficacy of different non-invasive tests. The areas under the receiver operating characteristic curves (AUROCs) for FIB-4, APRI, and NFS were 0.84, 0.85, and 0.76, respectively (Fig 1). Both FIB-4 and APRI demonstrated comparable and superior performance to NFS in predicting liver fibrosis among adults at risk, particularly in PLWH and those susceptible to MASLD.
Fig 1. The AUROCs of the FIB-4 score, APRI score, and NFS score for predicting significant fibrosis (F≥2) in MASLD Patients with PLWH
Fig 2. Implementation of a two-step approach involving non-invasive testing with FIB-4 and VCTE, as recommended by the American Gastroenterological Association (AGA) for MASLD in PLWH (n = 52).
Univariate analysis revealed several factors significantly associated with significant liver fibrosis: BMI (OR 1.17, p = 0.039), waist circumference (OR 1.28, p = 0.016), hip circumference (OR 1.24, p = 0.026), presence of dyslipidemia (OR 3.52, p = 0.031), AST levels (OR 1.08, p = 0.001), ALT levels (OR 1.03, p = 0.011), platelet count (OR 0.98, p = 0.017), and HDL-cholesterol level (OR 0.92, p = 0.024). After adjusting for age and sex in the multivariate analysis, higher BMI (aOR 1.24, p = 0.042), presence of dyslipidemia (aOR 3.96, p = 0.038), and elevated AST levels (aOR 1.19, p = 0.011) were independently associated with significant fibrosis (Table 3).
To further enhance the accuracy of prediction, we analyzed the implementation of a two-step pathway using the FIB-4 score followed by TE, as recommended by the American Gastroenterological Association (AGA)33, among PLWH with MASLD (n = 52). This approach improved risk reclassification for significant liver fibrosis, reducing the number of individuals at indeterminate risk from 12 out of 52 after the first step (FIB-4) to 7 out of 12 after the second step (VCTE), as illustrated in Fig 2. Consequently, among PLWH at risk for MASLD, patients were categorized into a low-risk group (40 patients, 76.9%), an indeterminate-risk group (7 patients, 13.5%), and a high-risk group (5 patients, 9.6%). This resulted in a reduced number of individuals in the indeterminate risk group, thereby minimizing the need for unnecessary liver biopsies.
DISCUSSION
In this analysis, the prevalence of MASLD among PLWH is notably high at 54.2%, comparable to rates observed in in Western countries.3,4,8,9 Additionally, approximately half of these patients are experiencing significant fibrosis. With the improvement of healthcare in Thailand and the availability of ART, the burden of MASLD and significant fibrosis in this population is anticipated to become increasingly significant in the future. Therefore, targeted efforts to screen and diagnose MASLD in population are imperative.
Our study found that the MASLD group had significantly higher anthropometric measures (body weight, BMI, waist circumference, and hip circumference) and higher levels of triglycerides and LDL cholesterol, along with higher liver stiffness and hepatic steatosis,
TABLE 3. Univariate and multivariate logistic regression analysis of factors associated with significant fibrosis (F≥2) in MASLD among PLWH.
Variable | Univariate | Multivariate | ||
OR (95%CI) | p-value | Adjusted OR (95%CI) | p-value | |
Age (years) | 1.05 (0.98-1.13) | 0.172 | ||
Male gender | 1.29 (0.42-4.04) | 0.657 | ||
BMI (kg/m2) | 1.17 (1.01-1.36) | 0.039 | 1.24 (1.01-1.52) | 0.042 |
BMI ≥30 | 2.78 (0.61-12.61) | 0.186 | ||
WC (inch) | 1.28 (1.05-1.57) | 0.016 | ||
HC (inch) | 1.24 (1.03-1.50) | 0.026 | ||
Presence of DM | 1.13 (0.38-3.38) | 0.829 | ||
Presence of DLP | 3.52 (1.12-11.06) | 0.031 | 3.96 (1.08-14.50) | 0.038 |
Presence of HT | 1.79 (0.56-5.66) | 0.325 | ||
AST (U/L) | 1.08 (1.03-1.14) | 0.001 | 1.19 (1.04-1.35) | 0.011 |
ALT (U/L) | 1.03 (1.01-1.04) | 0.011 | ||
Globulin (g/dL) | 3.83 (0.78-18.73) | 0.098 | ||
Platelet (x109/µL) | 0.98 (0.98-0.99) | 0.017 | ||
Triglyceride (mg/dL) | 1.00 (0.99-1.01) | 0.509 | ||
HDL-chol (mg/dL) | 0.92 (0.87-0.99) | 0.024 | ||
LDL-chol (mg/dL) | 0.99 (0.97-1.00) | 0.350 | ||
CAP (dB/m) | 0.99 (0.97-1.01) | 0.987 |
compared to the non-MASLD group. Although trends in metabolic comorbidities such as IFG, DM, DLP, and HT were higher in the MASLD group, they did not reach statistical significance. These findings highlight the metabolic profile associated with MASLD and emphasize the importance of managing these metabolic risk factors to prevent disease progression.
As acknowledged, the reported prevalence is influenced by the chosen cut-off of CAP, and notably, the optimal cut-off in PLWH is not yet to be determined. While studies in this population suggest using a CAP of 248 dB/m as a cut-off for hepatic steatosis,34,35 current practice guidelines for non-invasive tests recommend a cut-off of 275 dB/m, irrespective of HIV status.34,36 However, our study employed an even lower cut-off of 238 dB/m, likely resulting in an overestimation of MASLD prevalence. Given the limited data for the Asian
population, we opted for a lower cut-off CAP for use as a screening test to facilitate early detection, modification of risk factors, and adjustment of ART regimen to prevent liver complications.
In our study, we observed a lack of correlation between CAP and liver fibrosis. Several reasons contribute to this absence of direct correlation: 1) distinct pathological processes, as steatosis and fibrosis are separate yet often parallel occurrences; 2) variable progression rate, with liver diseases advancing at different rates in different individuals; 3) multiple factors influencing fibrosis, including inflammation, oxidative stress, and immune response, which may not be adequately captured by a marker primarily designed for fat content assessment; and 4) the heterogeneity of liver disease (e.g., non- alcoholic fatty liver disease, viral hepatitis, alcoholic liver disease, HIV infection), each potentially having
unique relationships between steatosis and fibrosis. While CAP may not predict liver fibrosis, it can serve as a valuable screening test, signaling the need for risk factor modification to prevent MASLD.
Characterizing PLWH based on MASLD, and liver fibrosis risk factors is crucial for identifying those at particular risk of developing advanced liver disease. In our study, significant MASLD and fibrosis were associated with high BMI, dyslipidemia, and a high level of AST, but not with HIV-related factors. These findings suggest that metabolic risk factors play a more substantial role in fibrosis development in PLWH, and targeted interventions such as weight management, diet modification, and exercise are crucial in preventing disease progression.
Overweight and obesity, as measured by BMI, is the main risk predictors for PLWH to develop fibrosis.37 In contrast to the past, where HIV patients were often cachectic due to opportunistic infections and ineffective ART, today, with effective ART and increased treatment accessibility, people living with HIV can lead longer and healthier lives. The causes of weight gain are similar to metabolic syndrome population, and certain ART regimens, such as tenofovir-alafenamid and integrase strand transfer inhibitors, can contribute to weight gain.10,38,39 Switching to tenofovir disoproxil fumarate has been independently associated with a lower risk of clinically significant weight gain, potentially slowing the development and progression of steatosis.10 Therefore, it is crucial for HIV patients to focus on diet control, limit alcohol consumption, and engage in exercise to manage body weight. While metabolic factors such as BMI, dyslipidemia, and AST levels were found to be significant predictors of fibrosis in our study, we cannot overlook the potential influence of HIV-specific factors, such as viral load, CD4+ T-cell count, and the duration of HIV infection. These factors, which were not significant in our analysis, may still play a crucial role in liver disease progression among PLWH, and future studies should explore their impact further.
Dyslipidemia is a common concern in PLWH, attributed to specific antiretroviral medications, chronic inflammation, and immune activation associated with HIV infection.40 Certain antiretroviral medications, especially protease inhibitors and some nucleoside reverse transcriptase inhibitors have been associated with increased level of TG and cholesterol.41 Effective management of dyslipidemia in HIV population is important to reduce cardiovascular risk but presents multiple challenges due to interactions between ART agents and lipid-lowering medications. Optimal medical therapy, including lipid- lowering therapy and changing ART regimens, is vital
for managing dyslipidemia and mitigating liver and cardiovascular risks.
While elevated AST levels were associated with hepatic steatosis and fibrosis in our study, it is important to recognize the limitations of using AST as a marker, as it lacks specificity for fibrosis. AST can be influenced by various factors, including ART-related mitochondrial injury, which could lead to overestimation of fibrosis severity.
Additionally, HIV and HBV or HCV coinfection or alcoholic consumption are additional predictors that warrant attention. HIV and viral hepatic coinfection promote hepatic injuries during MASLD through several mechanisms triggered by their persistent replication, enhanced inflammatory response and metabolic interference.42 Although not proven as predictors of significant liver fibrosis in our study due to the small sample size, HIV patients are strongly encouraged to manage viral hepatitis coinfections and limit alcohol consumption to reduce liver damage. We included patients with hepatitis B and C in this study because these are common comorbidities in PLWH, reflecting real-world clinical practice. Excluding them could have compromised the statistical power of the study. Similarly, we examined the impact of alcohol consumption, which showed no significant association with fibrosis development in our cohort. However, this may be due to the limited sample size, and the influence of alcohol should still be carefully considered when managing MASLD in PLWH.
Early detection and prompt treatment are crucial to slowing progressions and preventing liver fibrosis and hepatic complications. Out study confirms the benefit of non-invasive fibrosis markers, particularly the FIB-4 score and APRI, which exhibited the highest AUROCs. These markers can serve as screening tests to alert HIV patients to the risk of liver fibrosis and prompt treatment. Combining different non-invasive assessments, such as FIB-4 or APRI score and VCTE, according to the AGA pathway of a two-step approach,33 improves reclassification in the indeterminate group and reduces the need for unnecessary liver biopsy. It is essential to acknowledge that while liver biopsy remains the gold standard for assessing both steatosis and fibrosis, non- invasive methods are increasingly utilized due to their lower risk and greater patient acceptance. Although the AGA pathway is intended for advanced fibrosis, we adapted it for significant fibrosis due to the limited number of advanced fibrosis cases in our cohort. This adaptation allowed us to better capture the full spectrum of fibrosis in this population, but it will be acknowledged as a limitation. In addition to early detection, specific
clinical recommendations are essential. HIV patients should be counseled on lifestyle interventions such as diet modification, exercise, and alcohol reduction. Moreover, certain ART regimens associated with weight gain and dyslipidemia, such as integrase strand transfer inhibitors and tenofovir-alafenamide, should be re- evaluated. Switching to alternative regimens, such as tenofovir disoproxil fumarate, could mitigate some of the metabolic complications associated with MASLD progression.
This study has some limitations to acknowledge. First, the absence of liver biopsy data for correlation with TE findings is noteworthy. However, TE has demonstrated reasonable accuracy in estimating liver fibrosis and steatosis.27,28 Second, the cross-sectional nature of this study, lacking a longitudinal design, prevent the provision of data on cumulative exposure to specific ART regimens, which could potentially influence the development of hepatic steatosis and fibrosis over time. Third, variations in the defined cut-offs for significant fibrosis and hepatic steatosis have a determining impact on overall results and observed associations. Our utilization of cut-offs at 238 dB/m, in contrast to the recommended 275 dB/m by the European Association for the Study of the Liver (EASL) guideline36 for non-invasive tests, may have included patients with lesser degree of steatosis. Consequently, our results might not be directly comparable with other studies. Lastly, this study may not have comprehensively accounted for all confounding variables, such as herbs, medicines, and antioxidant supplements, as well as boarder factors like socioeconomic and concomitant treatment. Despite these limitations, our study underscores the importance of early detection and targeted interventions for MASLD in PLWH, especially in the context of rising metabolic comorbidities. Future studies should consider a prospective design, more stringent exclusion criteria, and a larger sample size to further validate these findings.
CONCLUSION
The prevalence of MASLD and significant fibrosis is notably high among PLWH. The predictors of significant fibrosis include higher BMI, dyslipidemia, and elevated AST levels. Additionally, fibrosis scoring systems exhibit good accuracy for fibrosis prediction. The integration of two-step approach involving a fibrosis scoring system and liver stiffness measurement has further improved the accuracy of prediction. To ascertain the effectiveness of this approach, future prospective longitudinal studies are warranted to identify early interventions and novel therapies within this population.
ACKNOWLEDGEMENTS
We would like to thank the healthcare professionals and the data acquisition team at Queen Savang Vadhana Memorial Hospital in Chonburi and King Chulalongkorn Memorial Hospital in Bangkok, Thailand, for their assistance in facilitating the survey and managing the demographic and laboratory data.
DECLARATION
This study was supported by the Fatty Liver Research Fund, Faculty of Medicine Foundation, Chulalongkorn University.
All authors declare no conflicts of interest.
T.P., S.T. - Conceptual design of the work; T.P., V.L., S.S., P.P., T.S., K.S., P.A., K.T., C.S., S.T. - Data
collection and data acquisition; T.P. - Data analysis and interpretation; V.L., T.P. - Drafting the manuscript; S.T.
- Critical revision of the manuscript; All authors - Final approval of the version to be published.
No artificial intelligence tools or technologies were used in the writing, analysis, or development of this research.
AGA: American Gastroenterological Association ALT: Alanine aminotransferase
aOR: adjusted odd ratio
APRI: Aspartate aminotransferase to platelet ratio index ART: Antiretroviral therapy
AST: Aspartate aminotransferase
AUROC: Areas under the receiver operating characteristic curves
BMI: Body mass index
CAP: Controlled attenuation parameter F: Fibrosis
FIB-4: Fibrosis-4 score HBV: Hepatitis B virus HCV: Hepatitis C virus
HIV: Human immunodeficiency virus LSM: Liver stiffness measurement
MASLD: Metabolic dysfunction-associated steatotic liver disease
NFS: Non-alcoholic fatty liver disease (NAFLD) fibrosis score
OR: odd ratio
PLWH: People living with HIV S: Steatosis
VCTE: Vibration-controlled transient elastography
REFERENCES
Teeraananchai S, Kerr S, Amin J, Ruxrungtham K, Law M. Life expectancy of HIV-positive people after starting combination antiretroviral therapy: a meta‐analysis. HIV Med. 2017;18(4): 256-66.
Teeraananchai S, Chaivooth S, Kerr SJ, Bhakeecheep S, Avihingsanon A, Teeraratkul A, et al. Life expectancy after initiation of combination antiretroviral therapy in Thailand. Antivir Ther. 2017;22(5):393-402.
Kalligeros M, Vassilopoulos A, Shehadeh F, Vassilopoulos S, Lazaridou I, Mylonakis E, et al. Prevalence and characteristics of nonalcoholic fatty liver disease and fibrosis in people living with HIV monoinfection: A systematic review and meta- analysis. Clin Gastroenterol Hepatol. 2023;21(7):1708-22.
Macías J, González J, Tural C, Ortega-González E, Pulido F, Rubio R, et al. Prevalence and factors associated with liver steatosis as measured by transient elastography with controlled attenuation parameter in HIV-infected patients. AIDS. 2014;28(9):1279-87.
Michel M, Labenz C, Armandi A, Kaps L, Kremer WM, Galle PR, et al. Metabolic dysfunction-associated fatty liver disease in people living with HIV. Sci Rep. 2023;13(1):9158.
Paula AA, Falcão MC, Pacheco AG. Metabolic syndrome in HIV-infected individuals: underlying mechanisms and epidemiological aspects. AIDS Res Ther. 2013;10(1):32.
Seth A, Sherman KE. Fatty liver disease in persons with HIV infection. Top Antivir Med. 2019;27(2):75-82.
Maurice JB, Patel A, Scott AJ, Patel K, Thursz M, Lemoine M. Prevalence and risk factors of nonalcoholic fatty liver disease in HIV-monoinfection. AIDS. 2017;31(11):1621-32.
Michel M, Labenz C, Wahl A, Anders M, Armandi A, Huber Y, et al. Prevalence and risk factors of nonalcoholic steatohepatitis with significant fibrosis in people with HIV. AIDS. 2022;36(12): 1665-74.
Bischoff J, Gu W, Schwarze-Zander C, Boesecke C, Wasmuth J-C, van Bremen K, et al. Stratifying the risk of NAFLD in patients with HIV under combination antiretroviral therapy (cART). EclinicalMedicine. 2021;40:101116.
Surial B, Mugglin C, Calmy A, Cavassini M, Günthard HF, Stöckle M, et al. Weight and metabolic changes after switching from tenofovir disoproxil fumarate to tenofovir alafenamide in people living with HIV. Ann Intern Med. 2021;174(6):758- 67.
Riebensahm C, Berzigotti A, Surial B, Günthard HF, Tarr PE, Furrer H, et al. Factors associated with liver steatosis in people with human immunodeficiency virus on contemporary antiretroviral therapy. Open Forum Infect Dis. 2022;9(11):ofac538.
Simon TG, Roelstraete B, Sharma R, Khalili H, Hagström H, Ludvigsson JF. Cancer risk in patients with biopsy‐confirmed nonalcoholic fatty liver disease: A population‐based cohort study. Hepatology. 2021;74(5):2410-23.
Kim G-A, Jeong S, Jang H, Lee DH, Joo SK, Kim W. Metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatotic liver disease with increase alcohol intake increase the risk of developing hepatocellular
carcinoma and incident or decompensated cirrhosis: a Korean nationwide study. Liver Cancer. 2023:1-12.
Musso G, Gambino R, Cassader M, Pagano G. Meta-analysis: Natural history of non-alcoholic fatty liver disease (NAFLD) and diagnostic accuracy of non-invasive tests for liver disease severity. Ann Med. 2011;43(8):617-49.
Boursier J, Hagström H, Ekstedt M, Moreau C, Bonacci M, Cure S, et al. Non-invasive tests accurately stratify patients with NAFLD based on their risk of liver-related events. J Hepatol. 2022;76(5): 1013-20.
Younes R, Caviglia GP, Govaere O, Rosso C, Armandi A, Sanavia T, et al. Long-term outcomes and predictive ability of non-invasive scoring systems in patients with non-alcoholic fatty liver disease. J Hepatol. 2021;75(4):786-94.
Henry L, Eberly KE, Shah D, Kumar A, Younossi ZM. Noninvasive tests used in risk stratification of patients with nonalcoholic fatty liver disease. Clin Liver Dis. 2023;27(2):373-95.
Wattacheril JJ, Abdelmalek MF, Lim JK, Sanyal AJ. AGA Clinical practice update on the role of noninvasive biomarkers in the evaluation and management of nonalcoholic fatty liver disease: Expert review. Gastroenterology. 2023;165(4):1080-8.
Wong VW-S, Adams LA, de Lédinghen V, Wong GL-H, Sookoian S. Noninvasive biomarkers in NAFLD and NASH — current progress and future promise. Nat Rev Gastroenterol Hepatol. 2018;15(8):461-78.
Loomba R. Role of imaging-based biomarkers in NAFLD: Recent advances in clinical application and future research directions. J Hepatol. 2018;68(2):296-304.
Manzano‐Nunez R, Rivera‐Esteban J, Navarro J, Bañares J, Sena E, Schattenberg JM, et al. Uncovering the NAFLD burden in people living with HIV from high‐ and middle‐income nations: a meta‐analysis with a data gap from Subsaharan Africa. J Int AIDS Soc. 2023;26(3):e26072.
Wong VW-S, Ekstedt M, Wong GL-H, Hagström H. Changing epidemiology, global trends and implications for outcomes of NAFLD. J Hepatol. 2023;79(3):842-52.
Wang D, Xu Y, Zhu Z, Li Y, Li X, Li Y, et al. Changes in the global, regional, and national burdens of NAFLD from 1990 to 2019: A systematic analysis of the global burden of disease study 2019. Front Nutr. 2022;9:1047129.
Lui G, Wong VW ‐S, Wong GL ‐H, Chu WC ‐W, Wong C ‐K, Yung IMH, et al. Liver fibrosis and fatty liver in Asian HIV‐ infected patients. Aliment Pharmacol Ther. 2016;44(4):411-21.
Nishijima T, Gatanaga H, Shimbo T, Komatsu H, Nozaki Y, Nagata N, et al. Traditional but not HIV-related factors are associated with nonalcoholic fatty liver disease in Asian patients with HIV-1 infection. PLoS One. 2014;9(1):e87596.
Sasso M, Beaugrand M, de Ledinghen V, Douvin C, Marcellin P, Poupon R, et al. Controlled attenuation parameter (CAP): A novel VCTETM guided ultrasonic attenuation measurement for the evaluation of hepatic steatosis: Preliminary study and validation in a cohort of patients with chronic liver disease from various causes. Ultrasound Med Biol. 2010;36(11):1825- 35.
Villani R, Lupo P, Sangineto M, Romano AD, Serviddio G. Liver ultrasound elastography in non-alcoholic fatty liver disease: A state-of-the-art summary. Diagnostics (Basel). 2023;13(7):1236.
Li Y, Chen Y, Zhao Y. The diagnostic value of the FIB-4 index for staging hepatitis B-related fibrosis: A meta-analysis. PLoS One. 2014;9(8):e105728.
De Matteis C, Cariello M, Graziano G, Battaglia S, Suppressa P, Piazzolla G, et al. AST to Platelet Ratio Index (APRI) is an easy-to-use predictor score for cardiovascular risk in metabolic subjects. Sci Rep. 2021;11(1):14834.
Treeprasertsuk S. NAFLD fibrosis score: A prognostic predictor for mortality and liver complications among NAFLD patients. World J Gastroenterol. 2013;19(8):1219-29.
Rinella ME, Lazarus J V, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78(6): 1966-86.
Kanwal F, Shubrook JH, Adams LA, Pfotenhauer K, Wai- Sun Wong V, Wright E, et al. Clinical care pathway for the risk stratification and management of patients with nonalcoholic fatty liver disease. Gastroenterology. 2021;161(5):1657-69.
Liu D, Shen Y, Zhang R, Xun J, Wang J, Liu L, et al. Prevalence and risk factors of metabolic associated fatty liver disease among people living with HIV in China. J Gastroenterol Hepatol. 2021;36(6):1670-8.
Jongraksak T, Sobhonslidsuk A, Jatchavala J, Warodomwichit D, Kaewduang P, Sungkanuparph S. Prevalence and predicting factors of metabolic-associated fatty liver disease diagnosed by transient elastography with controlled attenuation parameters in HIV-positive people. Int J STD AIDS. 2021;32(3):266-75.
Berzigotti A, Tsochatzis E, Boursier J, Castera L, Cazzagon N,
Friedrich-Rust M, et al. EASL Clinical practice guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update. J Hepatol. 2021;75(3):659-89.
Chandiwana NC, Siedner MJ, Marconi VC, Hill A, Ali MK, Batterham RL, et al. Weight gain after HIV therapy initiation: Pathophysiology and implications. J Clin Endocrinol Metab. 2024;109(2):e478-e87.
Sax PE, Erlandson KM, Lake JE, Mccomsey GA, Orkin C, Esser S, et al. Weight gain following initiation of antiretroviral therapy: Risk factors in randomized comparative clinical trials. Clin Infect Dis. 2020;71(6):1379-89.
Bares SH, Wu X, Tassiopoulos K, Lake JE, Koletar SL, Kalayjian R, et al. Weight gain after antiretroviral therapy initiation and subsequent risk of metabolic and cardiovascular disease. Clin Infect Dis. 2023;78(2):395-401.
Sugden M, Holness M. Management of dyslipidemia in HIV- infected patients. Clini Lipidol. 2011;6(4):447-62.
Calza L. Dyslipidaemia associated with antiretroviral therapy in HIV-infected patients. J Antimicrob Chemother. 2004;53(1): 10-14.
Iacob SA, Iacob DG. Non-alcoholic fatty liver disease in HIV/ HBV patients – a metabolic imbalance aggravated by antiretroviral therapy and perpetuated by the hepatokine/adipokine axis breakdown. Front Endocrinol. 2022;13:814209.