ABSTRACT
People infected with both hepatitis B virus (HBV) and hepatitis Delta virus (HDV) face a higher mortality risk than those mono‐infected with HBV. As unhealthy behaviours can influence liver disease progression, we compared the effects of various behavioural factors on all‐cause mortality among people with chronic hepatitis B (CHB), with or without chronic hepatitis Delta (CHD). We used 5‐year follow‐up data from people with CHB participating in the French ANRS CO22 HEPATHER cohort. A Cox proportional hazards model helped determine whether the pattern of risk factors for all‐cause mortality differed according to CHD status. Of the 3884 people included, 183 had CHD and 154 died during follow‐up. After multivariable adjustment, daily soft drink consumption significantly increased mortality risk in people with CHD and almost reached significance in those without CHD (adjusted hazard ratio (aHR) [95% CI]: 6.09 [2.40–15.48], p < 0.001, and 1.58 [0.97–2.56], p = 0.066 respectively). Moreover, past or current unhealthy alcohol use and tobacco smoking were both associated with a higher risk of mortality in all people with CHB (1.74 [1.09–2.79], p = 0.020, and 1.61 [1.13–2.31], p = 0.009 respectively). Daily soft drink consumption significantly increased all‐cause mortality in people with CHD. Unhealthy alcohol use and tobacco smoking were associated with a higher mortality risk in all people with CHB. Education about healthy eating and support for smoking cessation and alcohol reduction could greatly improve health and survival of people with CHB, with and without CHD.
Keywords: alcohol, HDV, mortality, soft drink, tobacco
Abbreviations
- (a)HR
(adjusted) hazard ratio
- ALT
alanine aminotransferase
- ANSM
Agence nationale de sécurité du médicament et des produits de santé (the French National Agency for the Safety of Medicinal Products and Health Products)
- BMI
body mass index
- CHB
chronic hepatitis B
- CHD
chronic hepatitis Delta
- CI
confidence interval
- HBeAg
antigen e of the hepatitis B virus
- HBV
hepatitis B virus
- HCV
hepatitis C virus
- HDV
hepatitis Delta virus
- IQR
interquartile range
- MASLD
metabolic dysfunction–associated steatotic liver disease
- PAF
population attributable fraction
- PY
person‐years
1. Introduction
People infected with hepatitis Delta virus (HDV) always have concurrent hepatitis B virus (HBV) infection [1], as HDV depends on the HBV protein envelope for survival and replication. Global HDV prevalence in people with HBV infection is estimated at between 4.5% and 14.6% [2, 3, 4], the highest prevalence rates being observed in regions where HBV is endemic [3]. This variability in prevalence primarily stems from the absence of routine HDV screening and the lack of rapid diagnostic tests, which limits opportunities for early diagnosis [2]. In 2016, HBV infection prevalence in France was 0.3% [5], and HDV affected 3.5% of the population of people with HBV infection [6].
Several studies have underscored the higher rates of liver‐related complications in people with HDV than in those monoinfected with HBV. These complications include a faster rate of liver fibrosis progression, a threefold higher risk of cirrhosis, a fivefold higher risk of hepatocellular carcinoma, and a higher risk of all‐cause mortality [7, 8, 9, 10, 11].
No study to date has compared the pattern of risk factors of mortality in people monoinfected with HBV versus those with HDV. While unhealthy behaviours are known to have a significant impact on the progression of liver disease and mortality in people infected with HBV [12, 13, 14, 15, 16], no study to date has explored the influence of behavioural risk factors on mortality in people infected with HDV, with the exception of alcohol use [7]. Given that this population faces a higher risk of liver disease progression and liver‐related fatalities, we hypothesise that unhealthy behaviours that impact liver health are associated with a higher all‐cause mortality risk in people infected with HDV than in those monoinfected with HBV. In this context, we aimed to assess and compare the effects of potential behavioural risk factors on all‐cause mortality in people with chronic hepatitis B, with and without HDV infection.
2. Material and Methods
2.1. Study Design
ANRS CO22 HEPATHER is a French, national, multicentre, prospective cohort study that included people infected with HBV and/or hepatitis C virus (HCV) between 2012 and 2018 in 32 expert hepatology centres. Written informed consent was obtained from each participant before enrolment. The protocol was developed in accordance with the Declaration of Helsinki and the French law for biomedical research (ClinicalTrials.gov: NCT01953458) [17]. It was approved by the ‘CPP Ile de France 3’ ethics committee (Paris, France) and the French National Agency for the Safety of Medicines and Health Products (ANSM).
2.2. Data Collection
Socio‐demographic, behavioural and bioclinical data were collected using an electronic case‐report form during the enrolment visit. The bioclinical data were subsequently updated during annual follow‐up visits.
2.3. Study Population and Study Period
The present analysis included people with chronic hepatitis B (CHB) status, either HBeAg‐negative or HBeAg‐positive [12], with or without chronic hepatitis Delta (CHD). We excluded people with past or current HCV coinfection, unavailable HDV infection status, or no follow‐up data (i.e., follow‐up visit or death) after their enrolment visit. Data were censored at 5 years after enrolment.
2.4. Study Outcome
The study outcome was the time between the enrolment visit and death, whatever the cause.
2.5. Explanatory Variables
The sociodemographic variables tested were sex, age, place of birth, educational level, living with a partner, and living in poverty (defined as reporting a household income per adult equivalent below the 2015 French poverty threshold (1015€/month)) [18]. Place of birth was categorised into Europe or America, North Africa or Middle East, Sub‐Saharan Africa and Asia.
The behavioural variables tested were past or current unhealthy alcohol use, past or current tobacco smoking, as well as daily coffee, tea, and soft drink consumption. Unhealthy alcohol use was defined as consuming more than two and three standard drinks/day for women and men respectively [19]. Soft drinks included all carbonated beverages, sweetened or not, and caffeinated or not.
The bioclinical variables tested were body mass index, diabetes, severe liver fibrosis (defined as an FIB‐4 score > 3.25 [20]), and HBV treatment status. The body mass index (BMI) was classed according to the World Health Organisation's guidelines into three categories: normal weight or underweight (BMI < 25), overweight (25 ≤ BMI < 30) and obesity (BMI ≥ 30) [21].
All explanatory variables were assessed at enrolment, except for HBV treatment, which was considered time‐varying.
2.6. Statistical Analyses
We compared study population characteristics between people with and without CHD using the Chi‐squared test for categorical variables and the Kruskal–Wallis test for continuous variables. Mortality rates per 100 person‐years (PY) were estimated and compared between these two sub‐populations using Fisher's exact test for incidence‐rate difference.
We used a Cox proportional hazards model to investigate the effect of CHD status and of behaviours on all‐cause mortality, after adjustment for sociodemographic and bioclinical factors. To assess whether the pattern of behavioural factors differed according to CHD status, we examined interaction effects between each factor and CHD status. Variables with a p < 0.2 in the univariable analyses were included in the multivariable analysis. The final multivariable model was constructed using a backward stepwise selection method with a significance level of 0.05. In order to better quantify the burden of behavioural factors contributing to mortality, population attributable fractions (PAF) were estimated for all the behavioural factors significantly associated with mortality risk in the final multivariable model.
A post hoc analysis describing the study population according to CHD status and daily soft drink consumption was performed. The Bonferroni method was applied to reduce the inflated error rate due to multiple pairwise comparisons.
All analyses were conducted using Stata version 17.0 for Windows (StataCorp LP, College Station, TX).
3. Results
3.1. Study Population Characteristics
The study population comprised 3884 people, including 183 (4.7%) people with CHD. Median (interquartile range [IQR]) follow‐up duration was 4.0 (3.6–4.1) and 3.9 (3.0–4.1) years, corresponding to 634 and 13,493,634 PY for people with CHD and those without CHD respectively.
Table 1 summarises the characteristics of the study population. Most of the sample were men (69.5%) and the median (IQR) age was 45 (35–57) years. Eighteen percent of people consumed soft drinks daily, 37.7% had past or current tobacco smoking, and 6.4% had past or current unhealthy alcohol use. People who consumed soft drinks daily were more likely to live in poverty than nondaily consumers. People with CHD were younger; they were also more likely to be born in Sub‐Saharan Africa, to live in poverty, and to have severe liver fibrosis than people without CHD. With regard to behaviours, there were no significant differences in terms of past or current unhealthy alcohol use (p = 0.462) and daily soft drink consumption (p = 0.650) between people with CHD and those without CHD. However, the latter were more likely to be former or current tobacco smokers (38.1% vs. 30.2%, p = 0.033) and to consume coffee daily (66.5% vs. 54.8%, p = 0.001), while the former were more likely to consume tea daily (52.3% vs. 38.6%) (Table 1).
TABLE 1.
Characteristics of the study population, overall and according to CHD status (ANRS CO22 HEPATHER cohort, N = 3884).
| Characteristics (% of missing values) | Study population (N = 3884) | People without CHD (N = 3701) | People with CHD (N = 183) | p‐value a | Deaths (N = 154) |
|---|---|---|---|---|---|
| N (%) or median (IQR) | N (%) or median (IQR) | N (%) or median (IQR) | N (%) or median (IQR) | ||
| Sex (0) | 0.307 | ||||
| Male | 2700 (69.5) | 2579 (69.7) | 121 (66.1) | 117 (76.0) | |
| Female | 1184 (30.5) | 1122 (30.3) | 62 (33.9) | 37 (24.0) | |
| Age (in years) (0) | 45 (35–57) | 45 (35–57) | 43 (34–52) | 0.007 | 61.5 (55–69) |
| Educational level (1.9) | 0.584 | ||||
| < high school diploma | 1885 (49.4) | 1802 (49.5) | 83 (47.4) | 88 (58.7) | |
| ≥ high school diploma | 1927 (50.6) | 1835 (50.5) | 92 (52.6) | 62 (41.3) | |
| Living with a partner (0.5) | 0.122 | ||||
| No | 1324 (34.2) | 1252 (34.0) | 72 (39.6) | 59 (38.6) | |
| Yes | 2542 (65.8) | 2432 (66.0) | 110 (60.4) | 94 (61.4) | |
| Place of birth (0) | < 0.001 | ||||
| Europe and America | 1572 (40.5) | 1534 (41.5) | 38 (20.8) | 100 (64.9) | |
| North Africa and Middle East | 449 (11.6) | 430 (11.6) | 19 (10.4) | 13 (8.4) | |
| Sub‐Saharan Africa | 1226 (31.6) | 1119 (30.2) | 107 (58.5) | 26 (16.9) | |
| Asia | 636 (16.4) | 617 (16.7) | 19 (10.4) | 15 (9.7) | |
| Living in poverty b (4.0) | < 0.001 | ||||
| No | 2063 (55.3) | 2001 (56.2) | 62 (36.7) | 93 (65.0) | |
| Yes | 1665 (44.7) | 1558 (43.8) | 107 (63.3) | 50 (35.0) | |
| Past/current unhealthy alcohol use c (0.3) | 0.462 | ||||
| No | 3625 (93.6) | 3456 (93.7) | 169 (92.3) | 121 (79.1) | |
| Yes | 246 (6.4) | 232 (6.3) | 14 (7.7) | 32 (20.9) | |
| Past/current tobacco smoking (0.2) | 0.033 | ||||
| No | 2415 (62.3) | 2288 (61.9) | 127 (69.8) | 70 (45.5) | |
| Yes | 1462 (37.7) | 1407 (38.1) | 55 (30.2) | 84 (54.5) | |
| Daily coffee consumption (2.1) | 0.001 | ||||
| No | 1293 (34.0) | 1213 (33.5) | 80 (45.2) | 49 (32.9) | |
| Yes | 2510 (66.0) | 2413 (66.5) | 97 (54.8) | 100 (67.1) | |
| Daily tea consumption (2.3) | < 0.001 | ||||
| No | 2304 (60.7) | 2220 (61.4) | 84 (47.7) | 99 (66.4) | |
| Yes | 1490 (39.3) | 1398 (38.6) | 92 (52.3) | 50 (33.6) | |
| Daily soft drink consumption (5.3) | 0.650 | ||||
| No | 3012 (81.9) | 2873 (81.9) | 139 (83.2) | 120 (82.8) | |
| Yes | 665 (18.1) | 637 (18.1) | 28 (16.8) | 25 (17.2) | |
| Severe liver fibrosis (9.8) | < 0.001 | ||||
| No (FIB‐4 ≤ 3.25) | 3276 (93.5) | 3140 (94.2) | 136 (80.5) | 96 (65.8) | |
| Yes (FIB‐4 > 3.25) | 227 (6.5) | 194 (5.8) | 33 (19.5) | 50 (34.2) | |
| Body mass index d (0.9) | 0.607 | ||||
| Normal or underweight | 1979 (51.4) | 1892 (51.6) | 87 (48.1) | 83 (53.9) | |
| Overweight | 1321 (34.3) | 1253 (34.2) | 68 (37.6) | 48 (31.2) | |
| Obesity | 549 (14.3) | 523 (14.3) | 26 (14.4) | 23 (14.9) | |
| Diabetes (0) | |||||
| No | 3533 (91.0) | 3360 (90.8) | 173 (94.5) | 0.084 | 120 (77.9) |
| Yes | 351 (9.0) | 341 (9.2) | 10 (5.5) | 34 (22.1) | |
| HBV treatment status (0) | 0.677 | ||||
| No | 1500 (38.6) | 1432 (38.7) | 68 (37.2) | 24 (15.6) | |
| Yes | 2384 (61.4) | 2269 (61.3) | 115 (62.8) | 130 (84.4) |
Abbreviations: BMI, body mass index; CHD, chronic hepatitis Delta; HBV, hepatitis B virus; IQR, interquartile range.
Chi‐squared test for categorical variables and Kruskal‐Wallis test for age.
Living in poverty was defined as a monthly household income per adult equivalent below the 2015 French poverty threshold (1015€).
Unhealthy alcohol use was defined as > 2 (> 3) standard drinks/day for women (men).
Normal weight or underweight corresponded to a BMI < 25, overweight to a BMI ≥ 25 but < 30, and obesity to a BMI ≥ 30.
During follow‐up, 154 participants died, 15 (8.2%) of whom had CHD (Table 1). The all‐cause mortality rate (95% CI) was higher in people with CHD than without CHD (2.36 [1.43–3.92] vs. 1.03 [0.87–1.22] per 100 PY, p = 0.008).
3.2. Factors Associated With All‐Cause Mortality
Among the interactions tested between CHD status and behavioural risk factors, the only significant interaction effect was observed for daily soft drink consumption after adjustment for age, severe liver fibrosis and other sociobehavioural factors (p = 0.044, data not shown).
In order to investigate the difference in the effect of daily soft drink consumption on mortality between people with and without CHD, we used a 4‐category variable combining CHD status and daily soft drink consumption in the analyses, adjusting for the same variables as for the previous multivariable model with interaction (Table 2). In the final multivariable model, the all‐cause mortality risk was six times higher in people with CHD who consumed soft drinks daily than in people without CHD who did not (adjusted hazard ratio (aHR) [95% CI]: 6.09 [2.40–15.48], p < 0.001). Interestingly, the association between daily soft drink consumption and the mortality risk was weaker among people without CHD and almost reached statistical significance (1.58 [0.97–2.56], p = 0.066). Moreover, past or current unhealthy alcohol use (1.74 [1.09–2.79], p = 0.020) and past or current tobacco smoking (1.61 [1.13–2.31], p = 0.009) were associated with higher mortality risk. In contrast, living with a partner had a protective effect on all‐cause mortality (Table 2). Of note, educational level, place of birth and living in poverty were not significantly associated with all‐cause mortality after multivariable adjustment; neither did they change the effect of significant variables.
TABLE 2.
Factors associated with all‐cause mortality, Cox proportional hazards model (ANRS CO22 HEPATHER cohort, N = 3884).
| Univariable analyses (N = 3884) | Multivariable analysis (N = 3302) | PAF (%) | |||
|---|---|---|---|---|---|
| HR (95% CI) | p‐value | aHR (95% CI) | p‐value | ||
| CHD status | |||||
| No (ref.) | 1 | ||||
| Yes | 1.85 (1.01–3.37) | 0.045 | |||
| Daily soft drink consumption | |||||
| No (ref.) | 1 | ||||
| Yes | 1.05 (0.68–1.62) | 0.822 | |||
| CHD status and daily soft drink consumption | 0.198 | 0.001 | |||
| People without CHD and without daily soft drink consumption (ref.) | 1 | 1 | |||
| People without CHD and with daily soft drink consumption | 0.98 (0.62–1.54) | 0.914 | 1.58 (0.97–2.56) | 0.066 | 5.4 |
| People with CHD and with daily soft drink consumption | 3.25 (1.06–9.95) | 0.039 | 6.09 (2.40–15.48) | < 0.001 | 1.8 |
| People with CHD and without daily soft drink consumption | 1.28 (0.58–2.81) | 0.543 | 0.69 (0.18–2.59) | 0.584 | −3.0 |
| Sex | |||||
| Male (ref.) | 1 | ||||
| Female | 0.77 (0.54–1.12) | 0.176 | |||
| Age (in years) | 1.08 (1.07–1.09) | < 0.001 | 1.07 (1.06–1.09) | < 0.001 | |
| Educational level | |||||
| < high school diploma (ref.) | 1 | ||||
| ≥ high school diploma | 0.66 (0.47–0.91) | 0.013 | |||
| Living with a partner | |||||
| No (ref.) | 1 | 1 | |||
| Yes | 0.79 (0.57–1.09) | 0.152 | 0.65 (0.45–0.92) | 0.016 | |
| Place of birth | |||||
| Europe and America (ref.) | 1 | < 0.001 | |||
| North Africa and Middle East | 0.48 (0.27–0.86) | 0.014 | |||
| Sub‐Saharan Africa | 0.33 (0.21–0.51) | < 0.001 | |||
| Asia | 0.38 (0.22–0.66) | 0.001 | |||
| Living in poverty a | |||||
| No (ref.) | 1 | ||||
| Yes | 0.68 (0.48–0.96) | 0.026 | |||
| Past/current unhealthy alcohol use b | |||||
| No (ref.) | 1 | 1 | |||
| Yes | 3.34 (2.18–5.13) | < 0.001 | 1.74 (1.09–2.79) | 0.020 | 9.7 |
| Past/current tobacco smoking | |||||
| No (ref.) | 1 | 1 | |||
| Yes | 1.94 (1.41–2.67) | < 0.001 | 1.61 (1.13–2.31) | 0.009 | 21.2 |
| Daily coffee consumption | |||||
| No (ref.) | 1 | ||||
| Yes | 1.10 (0.78–1.56) | 0.579 | |||
| Daily tea consumption | |||||
| No (ref.) | 1 | ||||
| Yes | 0.76 (0.54–1.07) | 0.121 | |||
| Severe liver fibrosis | |||||
| No (FIB‐4 ≤ 3.25) (ref.) | 1 | 1 | |||
| Yes (FIB‐4 > 3.25) | 7.46 (5.17–10.78) | < 0.001 | 3.67 (2.47–5.47) | < 0.001 | |
| Body mass index c | |||||
| Normal or underweight (ref.) | 1 | 0.494 | |||
| Overweight | 0.81 (0.57–1.17) | 0.259 | |||
| Obesity | 1.01 (0.64–1.59) | 0.972 | |||
| Diabetes | |||||
| No (ref.) | 1 | ||||
| Yes | 2.77 (1.85–4.14) | < 0.001 | |||
| HBV treatment status | |||||
| No (ref.) | 1 | ||||
| Yes | 2.44 (1.58–3.77) | < 0.001 | |||
Abbreviations: aHR, adjusted hazard ratio; BMI, body mass index; CHD, chronic hepatitis Delta; CI, confidence interval; HBV, hepatitis B virus; HR, hazard ratio; PAF, population attributable fraction.
Living in poverty was defined as a monthly household income per adult equivalent below the 2015 French poverty threshold (1015€).
Unhealthy alcohol use was defined as > 2 (> 3) standard drinks/day for women (men).
Normal weight or underweight corresponded to a BMI < 25, overweight to a BMI ≥ 25 but < 30 and obesity to BMI ≥ 30.
3.3. Proportion of All‐Cause Mortality Attributable to Behavioural Factors
The estimated PAFs highlighted that 9.7% of all‐cause mortality was attributable to past or current unhealthy alcohol use and 21.2% to past or current tobacco smoking. The burden of CHD and daily soft drink consumption each accounted for 1.8% of mortality (Table 2).
3.4. Post Hoc Analysis
Among people without CHD, those who consumed soft drinks daily were younger than those who did not (median age [IQR]: 38 [30–48] vs. 47 [37–58] years, p < 0.001) (Table 3). Moreover, they were more likely to be men, not live with a partner, and live in poverty than people without CHD with nondaily consumption of soft drinks. In contrast, they were less likely to have diabetes and HBV treatment than the latter group.
TABLE 3.
Characteristics of the study population according to CHD status and daily soft drink consumption (ANRS CO22 HEPATHER cohort, N = 3677).
| People without CHD and without daily soft drink consumption N = 2873) | People without CHD and with daily soft drink consumption N = 637) | People with CHD and with daily soft drink consumption (N = 28) | People with CHD and without daily soft drink consumption (N = 139) | |||||
|---|---|---|---|---|---|---|---|---|
| A | B | C | D | A vs. B | A vs. D | B vs. C | C vs. D | |
| Characteristics | N (%) or median (IQR) | N (%) or median (IQR) | N (%) or median (IQR) | N (%) or median (IQR) | p‐value a | p‐value a | p‐value a | p‐value a |
| Sex | < 0.001 | 0.949 | 1.000 | 0.279 | ||||
| Male | 1943 (67.6) | 501 (78.7) | 23 (82.1) | 86 (61.9) | ||||
| Female | 930 (32.4) | 136 (21.3) | 5 (17.9) | 53 (38.1) | ||||
| Age (in years) | 47 (37–58) | 38 (30–48) | 36.5 (30.5–46) | 43 (35–53) | < 0.001 | 0.008 | 1.000 | 0.482 |
| Educational level | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| 1428 (49.8) | 314 (49.4) | 11 (39.3) | 68 (49.6) | |||||
| ≥ high school diploma | 1437 (50.2) | 322 (50.6) | 17 (60.7) | 69 (50.4) | ||||
| Living with a partner | 0.010 | 1.000 | 0.817 | 0.542 | ||||
| No | 941 (32.8) | 250 (39.3) | 15 (53.6) | 50 (36.2) | ||||
| Yes | 1929 (67.2) | 386 (60.7) | 13 (46.4) | 88 (63.8) | ||||
| Place of birth | 1.000 | < 0.001 | 0.334 | 1.000 | ||||
| Europe and America | 1227 (42.7) | 255 (40.0) | 6 (21.4) | 28 (20.1) | ||||
| North Africa and Middle East | 335 (11.7) | 77 (12.1) | 2 (7.1) | 15 (10.8) | ||||
| Sub‐Saharan Africa | 820 (28.5) | 228 (35.8) | 19 (67.9) | 79 (56.8) | ||||
| Asia | 490 (17.1) | 77 (12.1) | 1 (3.6) | 17 (12.2) | ||||
| Living in poverty b | 0.020 | < 0.001 | 1.000 | 1.000 | ||||
| No | 1635 (57.9) | 323 (51.4) | 12 (44.4) | 46 (34.1) | ||||
| Yes | 1190 (42.1) | 305 (48.6) | 15 (55.6) | 89 (65.9) | ||||
| Past/current unhealthy alcohol use c | 1.000 | 1.000 | 1.000 | 1.000 | ||||
| No | 2689 (93.9) | 589 (92.5) | 26 (92.9) | 129 (92.8) | ||||
| Yes | 176 (6.1) | 48 (7.5) | 2 (7.1) | 10 (7.2) | ||||
| Past/current tobacco smoking | 1.000 | 0.072 | 1.000 | 0.667 | ||||
| No | 1773 (61.8) | 387 (60.7) | 16 (57.1) | 100 (72.5) | ||||
| Yes | 1098 (38.2) | 250 (39.3) | 12 (42.9) | 38 (27.5) | ||||
| Daily coffee consumption | 1.000 | 0.007 | 1.000 | 1.000 | ||||
| No | 973 (33.9) | 207 (32.6) | 10 (35.7) | 66 (47.5) | ||||
| Yes | 1898 (66.1) | 429 (67.4) | 18 (64.3) | 73 (52.5) | ||||
| Daily tea consumption | 0.905 | 0.001 | 1.000 | 1.000 | ||||
| No | 1784 (62.2) | 376 (59.1) | 15 (55.6) | 64 (46.0) | ||||
| Yes | 1085 (37.8) | 260 (40.9) | 12 (44.4) | 75 (54.0) | ||||
| Severe liver fibrosis | 0.149 | < 0.001 | 0.006 | 1.000 | ||||
| No (FIB‐4 ≤ 3.25) | 2423 (93.7) | 554 (96.2) | 21 (80.8) | 104 (80.6) | ||||
| Yes (FIB‐4 > 3.25) | 162 (6.3) | 22 (3.8) | 5 (19.2) | 25 (19.4) | ||||
| Body mass index d | 0.894 | 0.912 | 0.675 | 0.457 | ||||
| Normal or underweight | 1484 (51.9) | 307 (48.7) | 18 (64.3) | 63 (45.7) | ||||
| Overweight | 968 (33.9) | 231 (36.7) | 8 (28.6) | 53 (38.4) | ||||
| Obesity | 407 (14.2) | 92 (14.6) | 2 (7.1) | 22 (15.9) | ||||
| Diabetes | 0.001 | 0.984 | 1.000 | 1.000 | ||||
| No | 2582 (89.9) | 603 (94.7) | 27 (96.4) | 130 (93.5) | ||||
| Yes | 291 (10.1) | 34 (5.3) | 1 (3.6) | 9 (6.5) | ||||
| HBV treatment status | 0.001 | 1.000 | 0.255 | 1.000 | ||||
| No | 1064 (37.0) | 287 (45.0) | 7 (25.0) | 54 (38.8) | ||||
| Yes | 1809 (63.0) | 350 (55.0) | 21 (75.0) | 85 (61.2) |
Abbreviations: BMI, body mass index; CHD, chronic hepatitis Delta; HBV, hepatitis B virus; IQR, interquartile range.
Pairwise comparison for categorical variables and analysis of variance (ANOVA) test for age, with Bonferroni correction.
Living in poverty was defined as a monthly household income per adult equivalent below the 2015 French poverty threshold (1015€).
Unhealthy alcohol use was defined as > 2 (> 3) standard drinks/day for women (men).
Normal weight or underweight corresponded to a BMI < 25, overweight to a BMI ≥ 25 but < 30, and obesity to a BMI ≥ 30.
Among people with CHD, although there were differences in the characteristics of people with daily soft drink consumption and those with nondaily consumption, none were statistically significant. Specifically, the former were less likely to live in poverty and more likely to be receiving HBV treatment at the time of the study.
4. Discussion
To our knowledge, this is the first study to compare and explore behavioural factors associated with all‐cause mortality in people with CHB, with and without CHD. Two key findings emerged from our analysis. First, people with CHD who consumed soft drinks daily had a sixfold higher mortality risk than people without CHD with non‐daily consumption. Second, past or current unhealthy alcohol use and tobacco smoking had detrimental effects on all‐cause mortality risk in people with CHB, irrespective of CHD status. The absence of significant interactions between these two behaviours and CHD status shows that the risks of tobacco‐related and alcohol‐related mortality do not differ between people with and without CHD.
The effect of soft drink consumption has not been previously explored among people with CHD. However, its detrimental effects have been documented in the general population, specifically its association with cancer development [22]—including hepatocellular carcinoma [23, 24]—as well as with all‐cause and cardiovascular‐related mortality [25, 26]. Additionally, soft drink consumption is associated with both prevalent and incident metabolic dysfunction–associated steatotic liver disease (MALSD) [27, 28, 29, 30, 31], which is a risk factor for all‐cause mortality in people with CHB [32]. Surprisingly, in our study, neither BMI nor diabetes, both proxies for MASLD, were associated with mortality risk after multivariable adjustment. Moreover, there was no difference in terms of BMI between people who consumed soft drinks daily and those who did not. In addition, the prevalence of diabetes was lower in people with daily soft drink consumption than in those with non‐daily consumption, perhaps because those who consumed daily were younger. The deleterious effect of soft drink consumption may be attenuated by the protective effect of younger age in people without CHD; daily soft drink consumption was inversely associated with diabetes and HBV treatment in this group. Moreover, the higher risk of all‐cause mortality observed in people with CHD may be attributed to socioeconomic and/or lifestyle‐related factors. For example, it is likely that daily soft drink consumption captures the influence of low income and food inequity and insecurity [33], two factors associated with increased consumption of ultra‐processed foods high in salt and sugar. These unhealthy dietary habits are associated with cardiometabolic complications [33, 34, 35] and increased extra‐hepatic mortality [35, 36] in the general population. In our findings, living in poverty was associated with daily soft drink consumption in people without CHD but not in those with CHD, perhaps because of a lack of statistical power. Lifestyle interventions and dietary education are therefore crucial for people with CHB to address these risk factors.
While the effect of tobacco smoking on mortality has been explored among people with CHB, it has not been specifically studied in people with CHD. Previous studies demonstrated that tobacco smoking is associated with the progression of liver disease, including advanced liver fibrosis [37] and hepatocellular carcinoma [38, 39, 40], and higher mortality rates in people with CHB [12, 16]. Only a small number of studies have investigated the factors associated with mortality among people with CHD [7, 41], and none of the models used were adjusted for tobacco smoking. Consequently, data on the impact of tobacco on mortality risk in this population are lacking. Interventions promoting smoking cessation are needed for people with CHB, irrespective of their CHD status.
We found that past or current unhealthy alcohol use was associated with a higher risk of mortality in people with CHB, irrespective of CHD status. The association between unhealthy alcohol use and mortality has already been shown in people with CHB [12, 13, 42, 43, 44], as well as in those with CHD [7]. Unhealthy alcohol use is also associated with liver complications including liver fibrosis, cirrhosis and hepatocellular carcinoma in both people with CHB or CHD [7, 12, 37]. Screening, monitoring, and referral for adequate management of unhealthy alcohol use should become a priority in the clinical follow‐up of people with CHB.
4.1. Study Strengths and Limitations
Our study has several strengths. To the best of our knowledge, it is the first to compare behavioural risk factors of mortality in people with CHB, with and without CHD. Second, the large sample size from the ANRS CO22 HEPATHER cohort ensured good statistical power. Finally, all participants enrolled in the cohort were systematically screened for HDV infection. Few cohort studies to date have such extensive data collection in terms of the number of centres and participants included, the follow‐up duration, and detailed socio‐behavioural information.
The study also has limitations. First, as behavioural data were self‐reported, they may have been affected by social desirability bias (i.e., underreporting a specific behaviour, particularly regarding unhealthy alcohol use). However, any such underreporting would most likely have resulted in a unidirectional misclassification (e.g., classifying a portion of unhealthy alcohol users as nonusers) which would ultimately lead to an underestimation of the effects of the specific behaviours on mortality risk. Moreover, any underreporting bias would most likely affect estimates in people with and without CHD to the same extent. Accordingly, it would not have significantly impacted comparisons between the two sub‐populations. Second, as our data were only collected at the enrolment visit, we were not able to explore the effects of behavioural changes during follow‐up. Third, although ANRS CO22 HEPATHER has one of the largest representations of people with CHD of all the prospective cohorts to date, the number of such patients was still relatively low; accordingly, comparisons between people with CHD and those without CHD are somewhat limited. Fourth, we did not explore the specific causes of death due to a significant proportion of unknown causes of death in the cohort; accordingly, we could not compare liver‐related and non‐liver‐related mortality according to CHD status. Investigating whether socioeconomic and/or behavioural factors have an impact on specific‐cause mortality could provide further insights into the underlying mechanisms driving mortality differences between these two sub‐populations. This would contribute to a more nuanced understanding of the factors influencing mortality in people with CHD or without CHD. Fifth, we were unable to explore other lifestyle variables to understand whether the effect of daily soft drink consumption on mortality was linked to general unhealthy dietary habits or to other unhealthy behavioural factors. Finally, during the study period covered by the present analysis, the primary treatment for HDV infection was interferon‐alpha. Given the recent emergence of new HDV treatments awaiting approval (including bulevirtide and other drugs currently in development), complementary studies are essential to analyse the impact of these treatments on mortality risk among people with CHD. This would provide a more comprehensive understanding of the evolving landscape of HDV treatment and its effects on health outcomes.
5. Conclusion
Unhealthy alcohol use and tobacco smoking significantly increased all‐cause mortality risk in people with CHB, irrespective of CHD status. Daily soft drink consumption was associated with a higher mortality risk in people with CHD than in those without CHD. Education about healthy eating and support for smoking cessation and alcohol reduction could significantly improve the health and survival of people with CHB.
Author Contributions
M.B., P.C., C.R.: conceptualisation. C.R., P.C., M.B., C.P.: methodology. P.C., M.B., C.P., F.M.: validation. V.D.B.: data curation. C.R.: formal analysis. F.C., M.B. and the ANRS/AFEF HEPATHER study group: investigation. F.C.: project administration and funding acquisition. C.R., M.B. and P.C.: writing – original draft preparation. All authors reviewed the paper. All authors approved the final version, including the authorship list.
Conflicts of Interest
L.P. reports nonfinancial support from Gilead Science, Abbvie, NovoNordisk and Ipsen not related to this study. The other authors declare no conflicts of interest.
Acknowledgements
We thank the patients and clinicians at all the participating centres. We also thank INSERM‐ANRS MIE for sponsoring, funding, and conducting the ANRS CO22 HEPATHER cohort in collaboration with the French Association for the Study of the Liver (Association Française pour l'Etude du Foie: AFEF). Finally, our thanks to Jude Sweeney (Milan, Italy) for the English revision and copyediting of our manuscript.
ANRS/AFEF HEPATHER Study Group
Investigators.
Laurent Alric, Delphine Bonnet, Virginie Payssan‐Sicart, Chloe Pomes (CHU Purpan, Toulouse, France), Fabien Zoulim, Marianne Maynard, Roxane Bai, Lucie Hucault, François Bailly (Hospices Civils de Lyon, Lyon, France), François Raffi, Eric Billaud, David Boutoille, Maeva Lefebvre, Elisabeth André‐Garnier (Hôpital Hôtel‐Dieu, Nantes, France), Paul Cales, Isabelle Hubert, Adrien Lannes, Françoise Lunel, Jérôme Boursier (CHU Angers, Angers, France), Tarik Asselah, Nathalie Boyer, Nathalie Giuily, Corinne Castelnau, Giovanna Scoazec, Hélène Fontaine (Hôpital Beaujon, Clichy, France), Stanislas Pol, Emilie Rousseaud, Anaïs Vallet‐Pichard, Philippe Sogni, Lucia Parlati (Hôpital Cochin, Paris, France), Victor de Ledinghen, Juliette Foucher, Jean‐Baptiste Hiriart, Jancell M'Bouyou, Marie Irlès‐Depé (Hôpital Haut‐Lévêque, Pessac, Bordeaux, France), Marc Bourlière, Si Nafa Si Ahmed, Valérie Oules (Hôpital Saint Joseph, Marseille, France), Albert Tran, Rodolphe Anty, Eve Gelsi, Régine Truchi (CHU de Nice, Nice, France), Dominique Thabut, Saloua Hammeche, Joseph Moussali (Hôpital de la Pitié Salptétrière, Paris, France), Xavier Causse, Barbara De Dieuleveult, Brahim Ouarani, Damien Labarrière (CHR La Source, Orléans, France), Nathalie Ganne, Véronique Grando‐Lemaire, Pierre Nahon, Séverine Brulé, Betul ULKER (Hôpital Jean Verdier, Bondy, France), Dominique Guyader, Caroline Jezequel, Audrey Brener, Anne Laligant, Aline Rabot, Isabelle Renard (CHU Rennes, Rennes, France), François Habersetzer, Thomas F. Baumert, Michel Doffoel, Catherine Mutter, Pauline Simo‐Noumbissie, Esma Razi (Hôpitaux Universitaires de Strasbourg, Strasbourg, France), Jean‐Pierre Bronowicki, Hélène Barraud, Mouni Bensenane, Abdelbasset Nani, Sarah Hassani‐Nani, Marie‐Albertine Bernard (CHU de Nancy, Nancy, France), Georges‐Philippe Pageaux, Dominique Larrey, Magda Meszaros (Hôpital Saint Eloi, Montpellier, France), Sophie Metivier, Christophe Bureau, Thibault Morales, Jean Marie Peron, Marie Angèle Robic (CHU Purpan, Toulouse, France), Thomas Decaens, Bruno Froissart, Marie‐Noelle Hilleret, Charlotte Costentin, Théophile Gerster (CHU de Grenoble, Grenoble, France), Ghassan Riachi, Odile Goria, Fatima Paris, Hélène Montialoux (CHU Charles Nicolle, Rouen, France), Vincent Leroy, Giuliana Amaddeo, Anne Varaut, Mélanie Simoes, Rachida Amzal (Hôpital Henri Mondor, Créteil, France), Olivier Chazouillières, Tony Andreani, Bénédicte Angoulevant, Azeline Chevance, Lawrence Serfaty (Hôpital Saint‐Antoine, Paris, France), Jean‐Charles Duclos Vallée, Didier Samuel, Teresa Antonini, Audrey Coilly, Mariagrazia Tateo (Hôpital Paul Brousse, Villejuif, France), Armand Abergel, Maud Reymond, Chanteranne Brigitte, Buchard Benjamin, Léon Muti (Hôpital Estaing, Clermont‐Ferrand, France), Claire Geist, Guillaume Conroy, Raphaëlle Riffault (Centre Hospitalier Régional, Metz, France), Isabelle Rosa, Camille Barrault, Laurent Costes, Hervé Hagège (Centre Hospitalier Intercommunal, Créteil, France), Véronique Loustaud‐Ratti, Paul Carrier, Maryline Debette‐Gratien, (CHU Limoges, Limoges, France), Philippe Mathurin, Guillaume Lassailly, Elise Lemaitre, Valérie Canva, Sébastien Dharancy, Alexandre Louvet (CHRU Claude Huriez, Lille, France), Anne Minello, Marianne Latournerie, Marc Bardou, Thomas Mouillot (Dijon University Hospital, Dijon, France), Louis D'Alteroche, Didier Barbereau, Charlotte Nicolas, Laure Elkrief, Anaïs Jaillais (CHU Trousseau, 37044 Tours, France), Jérôme Gournay, Caroline Chevalier, Isabelle Archambeaud, Sarah Habes (CHU de Nantes, Nantes, France), Isabelle Portal (CHU Timone, Marseille, France), Moana Gelu‐Simeon, Eric Saillard, Marie‐Josée Lafrance, Lucie Catherine (CHU de Pointe‐à‐Pitre, Pointe‐à‐Pitre, Guadeloupe).
Methodology and Coordinating Centre
Fabrice Carrat (coordinator), Frederic Chau, Céline Dorival, Isabelle Goderel, Clovis Lusivika‐Nzinga, Marc‐Antoine Bellance, Jonathan Bellet, Priscilla Monfalet, Jessica Chane‐Teng, Sephora Bijaoui, Grégory Pannetier, François Téoulé, Jérôme Nicol, Rafika Bekhti (Sorbonne University & INSERM U1136—IPLESP, Paris, France).
Sponsors
Carole Cagnot, Anaïs Boston, Laura Nailler, Guillaume Le Meut (INSERM‐ANRS‐MIE, Paris, France), Alpha Diallo (Pharmacovigilance coordinator), Ventzislava Petrov‐Sanchez (coordinator).
Scientific Committee
‐ Voting members:
Marc Bourlière (Hôpital St Joseph, Marseille), Jérôme Boursier (CHU Angers, Angers, France), Fabrice Carrat (Scientific Coordinator, Hôpital Saint‐Antoine, Paris, France), Patrizia Carrieri (INSERM U912, Marseille, France), Elisabeth Delarocque‐Astagneau (Inserm UMR1181, Paris), Victor De Ledinghen (Hôpital Haut‐Lévêque, Pessac, Bordeaux, France), Céline Dorival (UPMC & INSERM U1136, Paris, France), Lucia Parlati (Hôpital Cochin, Paris, France), Slim Fourati (Hôpital Henri Mondor, Créteil, France), Chantal Housset (Inserm UMR‐S938 1 IFR65, Paris), Dominique Larrey (Hôpital Saint Eloi, Montpellier, France), Pierre Nahon (Hôpital Jean Verdier, Bondy, France), Georges‐Philippe Pageaux (Hôpital Saint Eloi, Montpellier, France), Ventzislava Petrov‐Sanchez (ANRS, Paris, France), Stanislas Pol (Principal Investigator, Hôpital Cochin, Paris, France), Mathias Bruyand (Agence Nationale de Santé Publique, Saint Maurice, France), Linda Wittkop (ISPED‐INSERM U897, Bordeaux, France), Fabien Zoulim (Hospices Civils de Lyon, Lyon, France), Jessica Zucman‐Rossi (Inserm U674/1162, Paris).
‐Non‐voting members:
Marianne L'hennaff (ARCAT‐TRT‐5‐CHV, France), Michèle Sizorn (SOS hépatites, France); one representative of INSERM‐ANRS‐MIE Pharmacovigilance team, Paris, France (Anaïs Boston, Alpha Diallo), Carole Cagnot (INSERM‐ANRS‐MIE, Paris, France), one member of Inserm Transfert, Paris, France (Alice Bousselet, Mireille Caralp), and one representative of each pharmaceutical company (MSD, Gilead, Abbvie).
Funding: The ANRS CO22 HEPATHER cohort is funded by INSERM‐ANRS MIE (France Recherche Nord&Sud Sida‐VIH Hépatites | Maladies Infectieuses Emergentes), ANR (Agence Nationale de la Recherche ANR‐11‐EQPX‐0021 and ANR‐19‐COHO‐0002), DGS (Direction Générale de la Santé) and MSDF, Janssen, Gilead, Abbvie, BMS, Roche.
Patrizia Carrieri and Marc Bourliere contributed equally to this study.
Data Availability Statement
The data that support the findings of this study are available upon request from the scientific committee of the ANRS CO22 HEPATHER cohort (contact: fabrice.carrat@iplesp.upmc.fr). The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available upon request from the scientific committee of the ANRS CO22 HEPATHER cohort (contact: fabrice.carrat@iplesp.upmc.fr). The data are not publicly available due to privacy or ethical restrictions.