Meta-analysis: Natural history of non-alcoholic fatty liver disease (NAFLD) and diagnostic accuracy of non-invasive tests for liver disease severity

Figures & data

Figure 1. Evidence acquisition flow diagram. Quality scores of included studies are provided as median (range).

Figure 2. Mortality in NAFLD compared with the general population.

Table I. Prospective studies assessing the natural history of NAFLD, grouped according to definition of NAFLD (biochemical, radiological, or histological).

NAFLD and diabetes
Author year (ref) STROBE score (failing items)	Follow-up (yrs); Population, age, sex (% M)	NAFLD definition	Outcome	Result
Vozarova 2002 (45) 21(g)	6.9 yrs; Community-based cohort of 451 Pima Indians, aged 18–50 yrs, 62% M	ALT	Diabetes incidence	OR for ALT elevation: 1.9 (95% CI 1.1–3.3) after adjustment for age, sex, body fat, insulin resistance and secretion, alcohol intake
Lee 2003 (26) 18(i,u,z,y)	4 yrs; Community-based cohort of 4088 healthy men working in a steel manufacturing company, aged 25–55 yrs	GGT	Diabetes incidence	OR for top versus lowest GGT quintile in drinkers ≤90 g/wk: 3.6 (95% CI 1.1–11.4). Adjusted for age, BMI, smoking, exercise, family history of diabetes mellitus, fasting blood glucose, alcohol intake
Sattar 2004 (27) 21(y)	4.9 yrs; Population-based cohort of 5974 men, mean age 55 yrs (West of Scotland Coronary Prevention Study)^a	ALT	Diabetes incidence	OR for top versus lowest ALT quartile: 2.04 (95% CI 1.16–3.58). Adjusted for age, BMI, smoking, systolic BP, total/HDL cholesterol ratio, pravastatin treatment, triglycerides, alcohol intake, fasting glucose, C-reactive protein, metabolic syndrome
Hanley 2004 (43) 21(u)	5.2 yrs; Population-based cohort of 906 subjects aged 40–69 yrs, 44% M (Insulin Resistance Atherosclerosis Study)^a	AST, ALT	Diabetes incidence	OR for top versus other ALT quartiles: 2.00 (1.22–3.28); for top versus other AST quartiles: 1.98 (1.23–3.17). Adjusted for age, sex, ethnicity, alcohol intake, waist, insulin sensitivity and secretion, plasma lipids
Nakanishi 2004 (28) 21(u)	7 yrs; Population-based cohort of 3260 Japanese male office workers, aged 35–59 yrs	GGT	Diabetes incidence	OR for top versus lowest GGT quintile: 2.44 (1.34–4.46). Adjusted for age, family history of diabetes, BMI, alcohol intake, smoking habits, physical activity fasting plasma glucose, white blood cell (WBC) count, other liver enzymes
Wannamethee 2005 (31) 22	5 yrs; Population-based cohort of 3500 men, aged 60–79 yrs (British Regional Heart Study)^a	ALT, GGT	Diabetes incidence	OR for highest versus lowest ALT quartile: 1.91 (1.01–3.60); for highest versus lowest GGT quartile: 2.69 (1.21–5.97). Adjusted for BMI, insulin resistance, C-reactive protein, alcohol intake, age, physical activity, smoking status
Nannipieri 2005 (44) 22	7 yrs; Population-based cohort of 1441 subjects, aged 35–64 yrs, 40% M (Mexico City Diabetes Study)	GGT	Diabetes incidence	OR for elevated GGT: 2.05 (1.39–3.03). Adjusted for age, sex, BMI, waist circumference, alcohol intake, fasting plasma insulin
Andre 2005 (36) 22	3 yrs; Population-based cohort of 4201 subjects, aged 30–65 yrs, 49% M (DESIR study)	GGT	Diabetes incidence	OR for top versus lowest GGT quartile: 5.0 (1.5–15.8) in men and 3.5 (0.9–14.2) in women. Adjusted for age,WHR, insulin resistance, alcohol intake, other liver enzymes, physical activity, smoking habits
Schindhelm 2005 (29) 21(y)	6 yrs; Population-based cohort of 1289 subjects, aged 50–74 yrs, 46% M (Hoorn Study)	ALT	Diabetes incidence	OR for upper versus lowest ALT tertile: 1.18 (0.66–2.11). Adjusted for age, sex, follow-up duration, BMI, waist circumference, alcohol intake, fasting plasma insulin, 2-h OGTT glucose levels
Meisinger 2005 (56) 21(g)	14.7 yrs; Population-based cohort of 3687 subjects (MONICA study), age range 25–64 yrs, 50% M	GGT	Diabetes incidence	OR for top versus lowest GGT quintile: 4.24 (2.11–8.54) in men and 2.41 (1.05–5.52) in women. Adjusted for age, hypertension, BMI, dyslipidemia, parental history of diabetes, regular smoking, alcohol intake, physical activity, education
Doi 2007 (35) 22	9 yrs; Population-based cohort of 1804 Japanese subjects, age 40–79 yrs, 40% M (Hisayama Study)	ALT, GGT	Diabetes incidence	OR for top versus lowest ALT quartile: 2.32 (0.91–5.92) for men, 4.40 (1.38–14.06) for women. OR for top versus lowest GGT quartile: 2.54 (1.03–6.26) for men, 5.73 (1.62–20.19) for women. Adjusted for age, total and abdominal adiposity, C-reactive protein, alcohol intake
Cho 2007 (39) 20 (u,z)	2 yrs; Cohort of 10038 Koreans from an urban and a rural community, age 51 yrs, 47% M	ALT	Diabetes incidence	OR for top versus lowest ALT quartiles: 2.20 (1.28–3.73) in males, 1.97 (1.03–3.77) in females. Adjusted for age, BMI, systolic blood pressure, family history of diabetes, C-reactive protein, smoking/alcohol/exercise status, fasting plasma glucose/triglyceride/HDL-C
Sato 2008 (46) 22	4 yrs; Community-based cohort of 8576 Japanese men, aged 40–55 yrs (Kansai Healthcare Study)	ALT, GGT	Diabetes incidence	In non-drinkers, highest AST, ALT, and GGT tertile had an OR of 2.02 (1.30–3.14), 2.37 (1.55–3.62), and 3.18 (1.75–5.76) for incident diabetes compared with moderate drinkers with the lowest GGT tertile. Adjusted for age, BMI, smoking habit, family history of diabetes, physical activity, fasting plasma glucose
Monami 2008 (42) 22	39.6 months; Population-based cohort of 2662 Italian subjects, aged 40–75 yrs, 43% M (FIBAR study)	GGT	Diabetes incidence	OR: 2.54 (1.26–5.11) for GGT > 40 IU/L. Adjusted for age, sex, metabolic syndrome
Jiamjarasrangsi 2008 (41) 21(u)	2.6 yrs; Community-based cohort of 2370 employees of a university hospital in Bangkok, mean age 43 yrs	ALT	Diabetes incidence	OR for top versus lowest ALT quartile: 6.14 (1.54–24.45). Adjusted for age, gender, education, alcohol consumption, smoking, family history of diabetes, BMI, systolic and diastolic blood pressure, fasting plasma glucose and uric acid, blood urea, follow-up duration, AST levels
Ford 2008 (40) 22	7 yrs; Population-based cohort of 2298 subjects, mean age 49.5 yrs, 38% M (from the EPIC-Potsdam Study)	ALT, GGT	Diabetes incidence	OR of 1.93 (1.27–2.92) for highest versus lowest ALT quintile and of 2.67 (1.63–4.27) for highest versus lowest GGT quintile. Adjusted for age, sex, education, smoking status, alcohol use, BMI, waist circumference, occupational activity, sports activity, systolic blood pressure, total and HDL-cholesterol, C-reactive protein, glucose
Goessling 2008 (58) 22	20 yrs; Community-based cohort of 2812 participants in the Framingham Offspring Heart Study, age 44 yrs, 44% M	AST, ALT	Diabetes incidence	OR of 1.33 (95% CI 1.17–1.53) per 1 gender-specific SD increase in logAST; OR of 1.48 (95% CI 1.30–1.69) per 1 gender-specific SD increase in logALT Adjusted for age, gender, smoking, alcohol intake, BMI, menopausal status, base-line plasma glucose, interim weight change
Fraser 2009 (47) 19(u,v,z)	7.3 yrs; Population-based cohort of 3041 women, aged 60–79 yrs (British Women's Heart and Health Study)	GGT	Diabetes incidence	OR 1.24 (1.04–1.48) per 1 SD change in serum GGT. Adjusted for age, alcohol intake, social class, physical activity, smoking, insulin resistance, waist-to-hip ratio, triglycerides, HDL-C, systolic blood pressure
Kim 2009 (37) 21(y)	5 yrs; Population-based cohort of 3556 subjects, mean age (range) 46 (20–79 yrs), 62% M	ALT, GGT	Diabetes incidence	OR of 4.31 (1.56–11.88) for highest versus lowest ALT quartile and of 3.07 (1.21–7.76) for highest versus lowest GGT quartile. Adjusted for sex, age, family history of diabetes, BMI, systolic blood pressure, alcohol consumption, smoking, exercise, fasting glucose, serum triglyceride, HDL-cholesterol, CRP
Adams 2009 (60) 22	11 yrs; Community-based cohort of 352 subjects, mean age 60 yrs, 68% M	ALT	Diabetes incidence	HR of 0.60 (0.21–1.20) after adjustment for age, BMI, waist, HOMA, fasting insulin, fasting glucose, triglycerides, alcohol intake
Okamoto 2003 (33) 19 (g,u,y)	10 yrs; Community-based cohort of 840 subjects, mean age 43 yrs, 56% M	US	Diabetes incidence	OR for NAFLD: 1.83 (0.95–3.51). Adjusted for age, sex, BMI, change in BMI, fasting plasma glucose, HbA1c, frequency of medical check-ups, alcohol intake, family history of diabetes
Shibata 2007 (34) 22	4 yrs; Community-based cohort of 3189 Japanese men, aged ≥ 40 yrs consuming < 20 g alcohol/day	US	Diabetes incidence	OR for NAFLD: 4.6 (3.0–6.9) after adjusting for age, BMI, metabolic syndrome, smoking and physical activity status
Kim 2008 (32) 21(u)	5 yrs; Community-based cohort of 5372 Koreans, mean age (range) 47 (20–79) yrs, 68% M	US	Diabetes incidence	OR for NAFLD: 3.92 (2.89–5.31) after adjusting for age, sex, alcohol intake, smoking, plasma lipids. The severity of ultrasonographic steatosis was associated with the risk of diabetes, even after excluding frequent drinkers.
Ekstedt 2006 (20) 20(g,u)	13.7 yrs; Community-based cohort of 129 patients with NAFLD, mean age 51 yrs, 67% M	Histology	Diabetes incidence	Diabetes occurred in 71% of patients with NASH and 46% of patients with SS (OR 2.98; 95% CI 1.23–7.22; P = 0.01)
NAFLD and incident fatal and non-fatal CVD events
Author	Follow-up (yrs); Population, age, sex (% M)	NAFLD definition	Outcome	Result/covariates in fully-adjusted models
Wannamethee 1995 (25) 21(g)	11.5 yrs; Population-based cohort of 7613 British middle-aged men	GGT	Fatal CHD	OR of highest quintile of 1.42 (95% CI 1.12–1.80). Adjusted for alcohol intake, BMI, smoking, pre-existing CHD, diabetes, antihypertensive medication, systolic and diastolic blood pressure, total and HDL-cholesterol, heart rate, blood glucose, physical activity, lung function (forced expiratory volume in 1 second (FEV1))
Ruttmann 2005 (11) 22	11.1 yrs (M), 12 yrs (F); Population-based cohort of 163944 Austrian subjects, aged 19–95 yrs, 46% M	GGT	Fatal CVD events	OR in 5th versus 1st GGT quintile of 1.57 (95% CI 1.30–1.91) in men and 1.68 (95% CI 1.26–2.25) in women for total mortality from CVD. Adjusted for age, body mass index, systolic blood pressure, cholesterol, triglycerides, glucose, smoking, work status, and year of examination
Lee DH 2006 (53) 21(u)	11.9 yrs; Population-based cohort of 28838 Finnish subjects, age 25–74 yrs, 48% M	GGT	Fatal and non-fatal CHD	Risk of CHD incrementally increased across GGT quintiles: OR for highest versus lowest GGT quintile: 1.57 (1.22–2.01) in men and 1.44 (1.03–2.02) in women. Adjusted for age, sex, BMI, diabetes, smoking habits, hypertension, alcohol intake, physical activity, total and HDL cholesterol
Meisinger 2006 (57) 21(g)	15.7 yrs; Population-based cohort of 1878 Austrian adults, age 25–64 yrs, 100% M (from MONICA study)	GGT	Fatal and non-fatal CHD	OR for the highest versus lowest GGT quartile: 2.34 (95% CI 1.23–4.44). Adjusted for age, diabetes, hypertension, smoking, education, TC/HDL-C ratio, physical activity, BMI, alcohol intake
Hozawa 2007 (30) 22	9.6 yrs; Population-based Japanese cohort of 2724 men and 4122 women, aged ≥ 30 yrs	GGT	Fatal CVD events	OR in highest versus lowest quartile: 2.88 (95% CI 1.14–7.28) in women. In the never-drinker women OR 1.62 (95% CI 1.11–2.37). No significant relationships observed in men. Adjusted for age, alcohol consumption, smoking, total and HDL-cholesterol, triglyceride, GOT, GPT, BMI, exercise, systolic BP, diabetes, antihypertensive medication
Lee DS 2007 (50) 22	19.1 yrs; Population-based cohort of 3451 subjects, mean age 44 yrs, 48% M (Framingham Heart Study)	GGT	Fatal and non-fatal CVD	OR in highest GGT quartile: 1.67 (1.25–2.22). Adjusted for age, sex, BMI, alcohol intake, smoking habits, physical activity, total and HDL cholesterol, diabetes, hypertension fasting plasma insulin, C-reactive protein
Schindhelm 2007 (49) 21(y)	10 yrs; Population-based cohort of 1439 subjects, aged 61 yrs, 45% M (Hoorn Study)	ALT	Fatal and non-fatal CHD events	OR for upper tertile versus lowest ALT tertile: 2.04 (1.35–3.10). Adjusted for age, sex, BMI, waist circumference, smoking, alcohol intake, blood pressure, LDL-cholesterol, triglycerides, glucose, insulin resistance
Goessling 2008 (58) 22	20 yrs; Community-based cohort of 2812 participants in the Framingham Offspring Heart Study, age 44 yrs, 44% M	AST, ALT	Fatal and non-fatal CVD events	OR of 0.97 (95% CI 0.88–1.07) per 1 gender-specific SD increase in logAST; OR of 1.05 (95% CI 0.96–1.16) per 1 gender-specific SD increase in logALT Adjusted for age, gender, systolic blood pressure, hypertension treatment, smoking, alcohol intake, BMI, diabetes, total/ HDL-cholesterol, lipid treatment
Monami 2008 (42) 22	39.8 months; Population-based cohort of 2662 Italian subjects, aged 54 yrs, 43% M (FIBAR study)	AST, GGT	Fatal and non-fatal CVD	OR 2.21 (0.98–5.43) for GGT > 40 IU/L; OR 6.5 (1.5–28.1) for AST > 40 IU/L. Adjusted for age, sex, metabolic syndrome
Ong2008 (21) 22	8.7 yrs; Population-based cohort of 11285 subjects, 47% M (NHANES-III and NHANES-III-Linked Mortality File)	ALT	Mortality from all causes, including liver disease and CVD	NAFLD had higher overall mortality (OR: 1.038, 95% CI 1.036–1.041) and liver-related mortality (OR: 9.32, 95% CI 9.21–9.43). Adjusted for age, gender, race, education, income, BMI, diabetes, hypertension
Dunn 2008 (22) 22	8.7 yrs; Population-based NHANES-III cohort of 7574 US subjects (980 NAFLD), aged 35–84 yrs, 41% M	ALT	Mortality from all causes, including CVD	NAFLD had higher age-adjusted all-cause mortality (OR: 1.37; 95% CI 0.98–1.91). In the 45–54 age group, NAFLD had significantly higher all-cause (OR: 4.40; 95% CI 1.27–13.23) and CVD mortality (HR: 8.15; 95% CI 2.00–33.20). Adjusted for age, gender, race, blood pressure, waist circumference, triglyceride, total and HDL-cholesterol, smoking, C-reactive protein, alcohol intake, physical activity, diabetes, statin use
Olynyk 2009 (59) 22	10 yrs; Population-based cohort of 3719 Australian subjects, age 53 yrs, 42% M (Busselton Health survey)	ALT	Fatal and non-fatal CVD events	HR 0.78 (0.54–1.13) for men and 1.09 (0.78–1.52) for women for higher versus lowest ALT tertile after adjustment for age, smoking, alcohol use, systolic blood pressure, fasting glucose, total and HDL-cholesterol, triglycerides, waist circumference
Fraser 2009 (51) 19(u,v,z)	7.3 yrs; Population-based cohort of 2961 women, aged 60–79 yrs (British Women's Heart and Health Study)	ALT, GGT	Fatal and non-fatal CVD events	OR 0.94 (0.65–1.37) per 1 SD change in serum ALT. OR 1.17 (0.93–1.48) per 1 SD change in serum GGT. Adjusted for age, alcohol intake, social class, physical activity, smoking, diabetes/insulin resistance, components of metabolic syndrome, BMI, triglycerides, HDL-C, systolic blood pressure
Jepsen 2003 (23) 20(g,h)	16 yrs; Population-based cohort (Danish National Registry of Patients) 1804 subjects with NAFLD (hospital diagnosis), 53% M	US	Mortality for all causes, including liver disease and CVD	NAFLD had an age-adjusted standardized mortality ratio (SMR) of 2.3 (95% CI 2.1–2.6) for all causes, of 19.7 (95% CI 15.3–25.0) for hepatobiliary disease, and of 2.1 (95% CI 1.8–2.5) for CVD. Adjusted for sex, diabetes, cirrhosis at base-line
Targher 2005 (52) 21(y)	5 yrs; Nested case-control study of 744 type 2 diabetic patients, age 65 yrs, 62% M	US	Fatal and non-fatal CVD	OR for NAFLD of 1.53, 95% CI 1.1–1.7. Adjusted for age, sex, smoking history, diabetes duration, HbAlc, metabolic syndrome, LDL cholesterol, liver enzymes, use of medications
Targher 2007 (48) 21(y)	6.5 yrs; Cohort of 2392 diabetic subjects aged 64 yrs, 56% M (Valpolicella Heart Study)	US	Fatal and non-fatal CVD	OR for NAFLD: 1.87 (1.2–2.6). Adjusted for age, sex, smoking habits, diabetes duration, HbAlc, metabolic syndrome, LDL-cholesterol, medications
Hamaguchi 2007 (55) 22	5 yrs; Community-based cohort of 1637 apparently healthy Japanese, aged 48 yrs, 60% M	US	Non-fatal CVD events	OR for NAFLD: 4.12(1.58–10.75) independently of age, sex, BMI, alcohol intake, smoking habits, LDL-cholesterol, each component of metabolic syndrome and metabolic syndrome
Haring 2009 (24) 22	7.2 yrs; Population-based cohort of 4160 German subjects, aged 50 yrs, 49% M (SHIP Study)	US + GGT	All-cause and CVD mortality	OR for all-cause mortality and CV mortality of ultrasonographic steatosis and highest GGT quintile in men: 1.98 (1.21–3.27) and 2.41 (1.05–5.55). The risk was not increased in women. Adjusted for age, waist circumference, alcohol consumption, physical activity, educational level, civil status, equalized income, functional co-morbidity index
Matteoni 1999 (19) 20(g,u)	18 yrs; Community-based cohort of 132 NAFLD patients, aged 53 yrs, 48% M	Histology	Overall and cause-specific deaths	Higher liver-related death in NASH (10%) compared to simple steatosis (2%). CVD death non-significantly higher in NASH compared to simple steatosis
Dam-Larsen 2003 (63) 20(g,u)	16.7 yrs; Population-based cohort (Danish National Registry of Patients) 109 subjects with non-alcoholic simple steatosis, age 19–80 (mean 39), 30% M	Histology	Overall and cause-specific deaths	No excess of overall (25%) and cause-specific mortality in non-alcoholic simple steatosis compared with the general Danish population
Adams 2005 (18) 21(g)	7.6 yrs; Community-based cohort of 420 patients with NAFLD aged 49 yrs, 49% M	US + histology	Overall and cause-specific mortality	Higher age- and sex-adjusted all-cause mortality (OR: 1.34, 95% CI 1.003–1.76). Liver disease was the 3rd leading cause of death (13% of all deaths) as compared with the 13th in the general population. Other causes of death were malignancy (28% of all deaths) and CVD (25% of all deaths)
Sanyal 2006 (64) 19(u,v,z)	10 yrs; Community-based cohort of 152 NASH-related and 150 age-, sex-, and race-matched HCV-related cirrhotic patients, aged 55 yrs, 49% M	Histology	All-cause deaths, including liver-related and CVD-related	Patients with NASH had a lower overall mortality (19% versus 29%; P = 0.04) but a higher CVD mortality rate (5%, 2nd cause of death, versus 1%, 5th cause of death; P = 0.03). Adjusted for age, BMI, diabetes, Child-Pugh score
Author year (ref) STROBE score (failing items)	Follow-up (yrs); Population, age, sex (% M)	NAFLD definition	Outcome	Result
Ekstedt 2006 (20) 20(g,u)	13.7 yrs; Community-based cohort of 129 patients with NAFLD, aged 51 yrs, 67% M	Histology	Fatal and non-fatal CVD events	CVD incidence and mortality of 29% and 16% in NASH, 3-fold and 2-fold higher, respectively, than in the SS and in general population
Rafiq 2009 (54) 21(g)	10.5–13 yrs; Community-based cohort of 173 patients with NAFLD, aged 50 yrs, 40% M	Histology	Fatal CVD events	CVD was the top cause of death (12.7% of cases) in the entire NAFLD cohort, with no difference between NASH (12.2%) and non-NASH (20%; P = 0.23)
Soderberg 2010 (17) 20(g,u)	24 yrs; Community-based cohort of 118 NAFLD patients (43% NASH), aged 47 yrs, 63% M	Histology	Overall and cause-specific deaths	Age, sex, and calendar period-adjusted mortality ratio of 1.69 (1.24–2.25) for NAFLD compared to the general population (even after excluding cirrhotic patients at base-line). Excess mortality of NAFLD was accounted for by NASH cohort, while steatosis survival rate was comparable to the general population. Cardiovascular disease, malignancy and liver disease were the top 3 causes of death

The author name is followed in the same box by the year of publications and by the 22-item STROBE score, with the item(s) not satisfied by the study indicated in parentheses:

(a) Title and abstract informative and balanced.

(b) Background/rationale stated in the introduction.

(c) Objective(s) specified in the introduction.

(d) Study design correctly presented early in the paper.

(e) Setting, locations, and relevant dates described.

(f) Eligibility criteria, methods of selection, and follow-up described.

(g) Diagnostic criteria, outcomes, exposures, predictors, potential confounders, and effect modifiers for all variables clearly defined.

(h) Sources of data and details of methods of measurement given for each variable of interest.

(i) Any efforts to address potential sources of bias described.

(l) How the study size was arrived at clearly explained.

(m) How quantitative variables were handled in the analyses clearly explained.

(n) All statistical methods, how missing data and loss to follow-up were addressed, any sensitivity analyses clearly described.

(o) Numbers of individuals at each stage of study reported.

(p) Characteristics of study participants, number of participants with missing data, average and total follow-up time clearly described.

(q) Outcome events or summary measures over time reported.

(r) Unadjusted and confounder-adjusted estimates and their precision (e.g. 95% CI) reported.

(s) Analyses of subgroups and interactions, and sensitivity analyses reported.

(t) Key results with reference to study objectives summarized.

(u) Limitations of the study discussed.

(v) Cautious overall interpretation of results given.

(z) Generalizability (external validity) of the study results discussed.

(y) Source of funding and role of the funders described.

^aProspective studies on diabetes incidence: some population-based prospective studies were not specifically designed to address the risk factors for developing diabetes, rather the relationship between insulin resistance, cardiovascular disease, and their risk factors (25,40,42); in these studies, the relationship between elevations in biochemical markers of liver injury and incident diabetes was analyzed, after adjusting for alcohol intake.

CT = computed tomography; CAD = coronary artery disease; CHD = coronary heart disease; HCV = Hepatitis C virus; IGT = impaired glucose tolerance; LV = left ventricular; MRS = magnetic resonance spectroscopy; OR = odds ratio; IMT = intima-media thickening; TC = Total cholesterol; US = ultrasonography; WHR = Waist-on-hips ratio.

Figure 3. NAFLD as a risk factor for incident CVD events. NAFLD was defined biochemically (increase in ALT and GGT levels, panel A) or by ultrasound or histology (panel B). Forest plot of comparison: Meta-analysis of multiple-adjusted results (OR of top versus bottom quantile of ALT and GGT, panel A, or ultrasound-histologically diagnosed NAFLD, panel B) as determinants of incident CVD, outcome: incident CVD. NAFLD was defined by biochemical criteria (ALT or GGT elevation, panel A) or by ultrasonographic-histological criteria (panel B). OR adjusted for multiple variables from different community-based or population-based prospective studies (Table I) were pooled and analyzed by random or fixed effect models. *Studies assessing fatal CVD events.

Figure 4. NAFLD as a risk factor for incident type 2 diabetes. NAFLD was defined biochemically (increase in ALT and GGT levels, panel A) or by ultrasound or histology (panel B). Forest plot of comparison: Meta-analysis of multiple-adjusted results (OR of top versus bottom quantile of ALT and GGT, panel A, or ultrasonographic NAFLD, panel B) as determinants of incident type 2 diabetes, outcome: incident type 2 diabetes. NAFLD was defined by biochemical criteria (ALT or GGT elevation, panel A) or by ultrasonographic criteria (panel B). OR adjusted for multiple variables from different community-based or population-based prospective studies (Table I) were pooled and analyzed by random or fixed effect models. *Studies assessing fatal CVD events.

Figure 5. Outcomes of NASH compared to simple steatosis for overall mortality (panel A), liver-related mortality (panel B), or cardiovascular disease (CVD) mortality (panel C). Forest plot of comparison of NASH versus simple steatosis, outcomes: overall mortality (panel A), liver-related mortality (panel B), CVD mortality (panel C). OR adjusted for variables from different community-based or population-based prospective studies (Table I) were pooled and analyzed by random or fixed effect models.

Table II. Panel A: Studies assessing biomarker panels validated for non-invasive assessment of the presence of NASH in patients with NAFLD. When > 1 study assessed the panel, summary estimates of diagnostic accuracy were calculated; when the panel was assessed in only 1 study, the AUROC is the summary estimate of the training and validation groups.

Study (ref)	n (% NASH)	Age (yrs)	% Females	BMI (kg/m^2)	% Diabetic	AUC (95% CI)	Cut-off kg/m^2	Se %	Sp %	LR+	LR−	STARD check-list (failing items); Comments
ELISA-detected plasma cytokeratin-18 (CK-18) fragments
Wieckowska 2006 (81)	44 (47%)	51	54	32	31	0.93 (0.83–1.00)	380 U/L	91	94	15.00	0.10	25
Yilmaz 2007 (84)	83 (54%)	49	46	30	15	0.78 (0.67–0.87)	121.6 U/L	60	96	5.44	0.15	25
Younossi 2008 (82)	101 (32%)	41	67	46	NR	0.71 (0.59–0.81)	174 U/L	64	87	4.92	0.41	23(k,r)
Diab 2008 (85)	99 (26%)	51	82	48	35	0.88 (0.77–0.99)	252 U/L	82	77	3.57	0.23	25
Tabesh 2008 (93)	70 (64%)	44	51	31	NR	0.83 (0.74–0.89)	275 U/L	47	91	5.88	0.57	23(k,r)
Malik 2009 (86)	95 (63%)	49	36	31	38	0.80 (0.76–0.84)	300 U/L	84	80	4.15	0.21	25
Feldstein 2009 (83)	139 (50%)	48	63	34	19	0.83 (0.75–0.91)	246 U/L	75	81	3.95	0.30	25
Musso 2010 (100)	125 (18%)	48	39	25	0	0.83 (0.80–0.90)	246 U/L	78	88	6.65	0.25	24 (k)
Crisan 2009 (101)	56 (52%)	47	55	30.2	20	0.79 (0.69–0.88)	479	63	84	3.94	0.44	25
Summary estimates	9 studies assessed CK-18 for NASH					0.82 (0.78–0.88)		78 (64–92)	87 (77–98)	6.00 (5.09–7.17)	0.25 (0.23–0.28)	STARD score (median, range): 25 (23–25)
NASHtest (Age, sex, height, weight, triglycerides, cholesterol, α2-macroglobulin, apolipoprotein A1, haptoglobin, AST, ALT, GGT, bilirubin)
Poynard 2006 (68)	257 (25%)	51	40	28.4	37	0.79 (0.67–0.86)	0.34	33	94	5.5	0.82	25; Patented algorithms owned by the FPHO. Intravascular hemolysis (reduced haptoglobin and increased bilirubin), other causes of bilirubin elevation (Gilbert, cholestasis), acute inflammation (increased haptoglobin) and abnormal apolipoprotein A1 levels limit its accuracy
NASH Predictive Index (NPI) Age, BMI, HOMA-IR, log(AST × ALT), female sex
Zein 2007 (75)	122 (69%)	49	66	NR	28	0.86 (0.80–0.93)	6	56	100	56.01	0.44	20(j,k,p,q,w); Unusually high prevalence of disease in this cohort. Patients on insulin were excluded since insulin treatment can affect results
Obesity-related NASH Diagnostics (Serum cleaved CK-18, adiponectin, resistin)
Younossi 2008 (76)	101 (32%)	41	67	46	NR	0.85 (0.77–0.92)	0.27720.34990.2075	9577100	708747	3.215.931.90	0.060.260.00	23(k,r); It measures plasma adipokines adiponectin and resistin and intact and cleaved CK-18, markers of hepatocyte apoptosis, in a cohort of morbidly obese subjects undergoing surgery
MASH Clinical score for morbid obesity (Hypertension, diabetes, AST ≥ 27 IU/L, ALT ≥ 27 IU/L, sleep apnea, non-black race)
Campos 2008 (77)	200 (32%)	43	84	48	26	0.80 (NR)	6	22	99	22.15	0.79	25; Four risk categories for NASH were identified based on six readily available variables. A total score ≥ 6 predicted NASH with very high probability, but its performance in excluding NASH was fair-to-low
MAFIC score (serum ferritin, serum fasting insulin, serum type IV collagen 7s)
Sumida 2010 (105)	619 (55%)	52	49	27.8	41	0.79 (NR)	> 0≥ 2	9058	4490	1.615.80	0.230.47	25; Multicenter Japanese study from 8 hepatology centers

LR+ = Positive likelihood ratio; LR− = Negative likelihood ratio.

Table II. Panel B: Studies assessing biomarker panels validated for non-invasive assessment of fibrosis in patients with NAFLD. When > 1 study assessed the panel, summary estimates of diagnostic accuracy were calculated.

Study	n	Age (yrs)	% Females	BMI	% Diabetic	Diagnostic end-point (prevalence)	AUC (95% CI)	Cut-off	Se %	Sp %	LR+	LR−	STARD check-list (failing items); Comments
Fibrotest (α2-macroglobulin, apolipoprotein A1, haptoglobin, GGT, total bilirubin)
Ratziu 2006 (78)	267	51	42%	48% with BMI > 27	34%	Fibrosis ≥ F2 (27% patients)Fibrosis ≥ F3 (13% patients)	0.81 (0.74–0.86)0.88 (0.82–0.92)	0.300.700.300.70	77159225	77987197	3.357.503.178.33	0.290.870.110.77	25; Patented formula. Limitations are the same as for NASHtest. The 2 proposed cut-off values of 0.30 and 0.70 left in-between 33% of patients, in whom significant liver fibrosis could not be ruled in or out
Adams 2008 (79)	119	49	46%	45% obese	32	Fibrosis ≥ F2 (42%)	0.72 (0.62–0.81)	0.300.70	7370	6960	2.411.82	0.390.50	19 (c,e,h,j,k,n)
Summary estimates	2 studies assessed Fibrotest for fibrosis F2					Fibrosis ≥ F2	0.78 (0.72–0.85)	0.300.70	76 (70–84)32 (24–39)	74 (69–81)87 (81–94)	2.922.46	0.320.78	STARD score: 19–25
NAFLD fibrosis score (Age, BMI, hyperglycemia, platelet count, albumin, AST/ALT ratio)
Angulo 2007 (80)	733	48	47	32	30	Fibrosis ≥ F3 (27%)	0.88 (0.85–0.92)	−1.455 + 0.676	8047	7498	3.1024.01	0.270.54	25; Advanced fibrosis was diagnosed or excluded in 75% of cases, leaving 25% of patients with an indeterminate probability
Qureshi 2008 (86)	331	41	83	48	35	Fibrosis ≥ F3 (16%)	0.82 (NR)	−1.455+0.676	9649	3884	1.513.11	0.110.61	24(f); 47% of patients with an indeterminate probability of advanced fibrosis
Wong 2008 (85)	162	46	41	28.5	57	Fibrosis ≥ F3 (11%)	0.64 (0.49–0.79)	−1.455+0.676	390	8199	2.100.00	0.751.01	24 (p); 20% of patients with an indeterminate probability of advanced fibrosis
Guha 2008 (81)	91	49	71	35.6	58	Fibrosis ≥ F3 (23%)	0.89 (0.81–0.97)	−2.382+0.833	9177	5993	2.2410.74	0.150.25	24 (k); A subgroup of the original cohort of Guha study
Guturu 2008 (88)	118	50	48	31	NR	Fibrosis ≥ F3 (25%)	0.67 (0.58–0.75)	−1.455+0.676	4868	7694	212.21	0.680.33	24(f); 58% of subjects indeterminate probability of advanced fibrosis
Harrison 2008 (87)	92	NR	51	33	NR	Fibrosis ≥ F3 (22%)	0.74	−1.455+0.676	90	29	1.30	0.34	23(f,k)
Cales 2009 (83)	235	51	26	29	24	Fibrosis ≥ F2	0.88 (0.83–0.94)	−1.455+0.676	61	96	16.25	0.41	24 (k)
Ruffillo 2009 (103)	138	49	51	31.3	23	Fibrosis ≥ F3 (26%)	0.68 (0.57–0.78)	−1.455+0.676	5414	27100	0.7414.00	1.70.86	22(f,h,k); Indeterminate probability in 30% of cases
Parker 2009 (102)	50	57	38%	33.4	46	Fibrosis ≥ F2 (12%)	0.85 (0.80–0.91)	−1.455+0.676	100100	4493	1.7914.29	0.000.00	22(f,h,k)
						Fibrosis ≥ F3 (10%)	0.87 (0.81–0.93)	−1.455+0.676	100100	4293	1.7214.29	0.000.00
Wong 2010 (77)	247	51	45	28.0	36	Fibrosis ≥ F2 (43%)	0.62 (0.58–0.72)	−1.455+0.676	5512	7096	1.833.00	0.640.92	25; 34% of patients with indeterminate probability
						Fibrosis ≥ F3 (24%)	0.75 (0.67–0.83)	−1.455+0.676	7318	7096	2.434.50	0.390.85
Fujii 2009 (106)	122	59	61	27.0	NR	Fibrosis ≥ F2 (68%)	0.70 (0.65–0.77)	−1.455+0.676	6820	74100	2.6220.00	0.430.80	24(f)
						Fibrosis ≥ F3 (37%)	0.76 (0.70–0.85)	−1.455+ 0.676	8733	6497	2.4211.00	0.200.69
Sumida 2010 (105)	588	52	49	27.8	41	Fibrosis ≥ F3 (11%)	0.85 (NR)	−1.455+ 0.676	9233	6295	2.426.14	0.120.71	25; Multicenter Japanese study from 8 hepatology centers; 36% with indeterminate result
Pimentel 2010 (122)	158	36	77	41	16	Fibrosis ≥ F3 (14%)	0.85 (NR)	−1.455+ 0.676	9547	8095	4.759.4	0.060.55	24(f); 16% with indeterminate results
Summary estimates	4 studies assessed NAFLD fibrosis score for fibrosis ≥ F2					Fibrosis ≥ F2	4	−1.455+0.676	8236	7090	2.733.60	0.260.71	STARD score (median, range): 24 (22–25)
	13 studies (3064 participants) assessed NAFLD fibrosis score for fibrosis ≥ F3					Fibrosis ≥ F3	0.85 (0.81–0.90)	−1.455+ 0.676	90 (86–95)64 (59–70)	60 (56–65)97 (94–99)	2.2520.3	0.170.37
BARD score BMI ≥ 28 (1 point), AST/ALT ratio ≥ 0.8 (2 points), diabetes (1 point)
Harrison 2008 (87)	827	49	51	33	33	Fibrosis ≥ F3 (22%)	0.81 (NR)	2	91	66	2.68	0.13	23(f,k); Combines 3 variables in a weighted sum. The performance was comparable to the NAFLD fibrosis score in a subgroup from the same cohort and was particularly high in non-diabetic subjects, as a result of the increased OR of AST/ALT ratio in these patients: OR 15.6 (95% CI 8.4–28.9) in non-diabetic versus 4.3 (95% CI 2.5–7.4) in diabetic subjects. AST/ALT ratio increases by decreased clearance of AST as sinusoidal fibrosis progresses
Fujii 2009 (106)	122					Fibrosis ≥ F2 (69%)	0.63 (0.58–0.69)	2	50	76	2.08	0.66	24(f)
						Fibrosis ≥ F3 (38%)	0.69 (0.65–0.76)	2	65	72	2.32	0.49
Wong 2010 (77)	265	51	45	28.0	36	Fibrosis ≥ F2 (43%)	0.59 (0.54–0.65)	2	51	67	1.55	0.73	25
						Fibrosis ≥ F3 (23%)	0.64 (0.60–0.69)	2	62	66	1.82	0.58
Parker 2009 (102)	50					Fibrosis ≥ F2 (12%)	0.60 (0.55–0.66)	2	83	36	1.29	0.47	22(f,h,k)
						Fibrosis ≥ F3 (10%)	0.60 (0.54–0.65)	2	80	36	1.25	0.56
Ruffillo 2009 (103)	138	49	51	31.3	23	Fibrosis ≥ F3 (26%)	0.67 (0.56–0.77)	2	51	77	2.22	0.64	22(f,h,k)
Raszeja 2010 (104)	104	50	35	29.8	18	Fibrosis ≥ F3 (14%)	0.82	2	87	73	3.18	0.18	22(f,h,k)
Summary estimates	3 studies assessed BARD score for fibrosis ≥ F2					Fibrosis ≥ F2	0.60 (0.55–0.64)	2	61 (54–68)	60 (53–67)	1.53	0.66	STARD score (median, range): 24 (22–25)
	6 studies assessed BARD score for fibrosis ≥ F3					Fibrosis ≥ F3	0.78 (0.72–0.84)	2	72 (60–84)	64 (56–72)	2.00	0.44	STARD score (median, range): 23 (22–25)
Enhanced Liver Fibrosis (ELF) panel HA, PIIINP, TIMP-1
Guha 2008 (81)	192	49	36	32	31	Any fibrosis (stage 1–4; 59% patients)Fibrosis ≥ F2 (40% patients)Fibrosis ≥ F3 (23% patients)	0.76 (0.69–0.83)0.82 (0.75–0.88)0.90 (0.84–0.96)	−0.2070−0.10680.3576	617080	808090	3.103.508.01	0.490.380.22	24 (k); Combines plasma levels of 3 proteins and proteinases involved in early stages of hepatic collagen deposition. Only 14% of the patients left in the indeterminate gray area for advanced fibrosis, thus reducing the need for liver biopsy. The diagnostic performance for milder fibrosis is poor. This panel has been validated in a cohort of 112 pediatric NAFLD patients
Guha 2008 (combined panel) (81)	91	NR	NR	NR	NR	Any fibrosis (stage 1–4)Fibrosis ≥ F2Fibrosis ≥ F3	0.84 (0.76–0.92)0.93 (0.88–0.99)0.98 (0.96–1)	−5.002−0.995−0.2826	928991	528696	1.906.4022.81	0.150.130.09	24 (k); The combination of NAFLD fibrosis score plus ELF was tested in a subgroup of 91 patients from the original Guha cohort, yielding a higher diagnostic performance than either panel alone
Fibrometer Age, body weight, glucose, AST, ALT, ferritin, platelet count
Cales 2009 (83)	235	51	26%	29	24%	Fibrosis ≥ F2 (28%)Fibrosis ≥ F3 (19%)	0.94 (0.91–0.98)0.94 (NR)	≤ 0.611≥ 0.715NR	79NR	96NR	19.05NR	0.22NR	24 (k); Fibrometer has higher diagnostic accuracy for significant fibrosis than NAFLD fibrosis score (AUC 0.88; 95% CI 0.83–0.94; P = 0.008), while its accuracy for advanced fibrosis was comparable to NAFLD fibrosis score (AUC 0.93; P = 0.294)
Transient elastography (Fibroscan)
Kelleher 2006 (74)	129	49	51	31	31	Fibrosis ≥ F2 (50%)	0.86	8.7	81	78	3.68	0.24	22(e,f,w)
						Fibrosis ≥ F3		NA	NA	NA	NA	NA
Yoneda 2008 (75)	102	52	59	26.6	0	Fibrosis ≥ F2 (53%)	0.87	6.7	88	74	3.39	0.16	25; failure of LSM in 5% patients, all obese. Median
						Fibrosis ≥ F3 (28%)	0.90	9.8	85	81	4.47	0.19	LSM 7.8 kPa, IQR 18%
Nobili 2008 (76)	50	13	38	26	0	Fibrosis ≥ F2 (24%)	0.99 (0.92–0.99)	7.4	100	92	12.7	0.0	25; Success rate 89%; IQR/M 16%
						Fibrosis ≥ F3 (10%)	1 (0.94–1)	10.2	100	100	∞	0.0
Chang 2008 (79)	19	53	58	25.0	0	Fibrosis ≥ F2 (41%)	0.84 (0.79–90)	7.0	79	76	3.3	0.27	25; Success rate 88%; IQR 1.21
						Fibrosis ≥ F3 (23%)	0.93 (0.89–0.96)	8.7	84	83	5.0	0.19
Obara 2008 (78)	17	50	35	27.6	0	Fibrosis ≥ F2 (26%)	0.99 (0.94–1.0)	9.0	100	93	14.29	0.00	25; Success rate > 60% IQR NR
						Fibrosis ≥ F3 (21%)	0.93 (0.82–1.05)	11.0	75	93	10.71	0.27
Wong 2010 (77)	246	51	45	28.0	36	Fibrosis ≥ F2 (47%)	0.88 (0.78–0.97)	8.1	91	76	3.79	0.12	25; Success rate: 87% (failures of LSM mostly in patients
						Fibrosis ≥ F3 (29%)	0.99 (0.96–1.01)	15.8	100	95	20.00	0.00	with BMI > 30); IQR/M 17%
Summary estimates	6 studies assessed Fibroscan for fibrosis ≥ F2					Fibrosis ≥ F2	0.84 (0.79–90)	7.0	79	76	3.3	0.27	STARD score 25 (22–25)
	5 studies assessed Fibroscan for fibrosis ≥ F3					Fibrosis ≥ F3	0.94 (0.86–0.99)	8.7	94 (88–99)	95 (89–99)	18.8	0.06	STARD score 25 (25–25)

In the same row, the name of the score and its components are given.

Fibrosis was assessed on a 5-stage histological scale validated by NASH Clinical Research Network: 0 = absent; F1 = perisinusoidal or periportal; F2 = perisinusoidal and periportal; F3 = bridging; F4 = cirrhosis.

Collectively, fibrosis stage ≥ 2 is named significant; fibrosis stage ≥ 3 is named advanced (see text).

Summary outcome estimates are calculated including the training cohort and internal and external validation cohorts. If the score was assessed in only 1 study,^a then training and internal validation cohorts were included.

NAFLD fibrosis score: −1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m²) + 1.13 × impaired glucose tolerance/diabetes (yes = 1; no = 0) + 0.99 × AST/ALT ratio – 0.013 × platelets (× 10⁹/L) – 0.66 × albumin (g/dL).

ELF panel: −7.412 + (ln(HA) × 0.681) + (ln(PIIINP) × 0.775) + (ln(TIMP-l) × 0.494).

Fibrometer: 0.4184 × glucose (mmol/L) + 0.0701 × AST (IU/L) + 0.0008 × ferritin (μg/L) – 0.0102 × platelet count (× 10⁹/L) – 0.0260 × ALT (IU/L) + 0.0459 × body weight (kg) + 0.0842 × age (yrs) + 11.6226.

STARD check-list:

(a) TITLE/ABSTRACT/KEYWORDS. Identify the article as a study of diagnostic accuracy (recommend MeSH heading ‘sensitivity and specificity’).

(b) INTRODUCTION. State the research questions or study aims, such as estimating diagnostic accuracy or comparing accuracy between tests or across participant groups.

STUDY METHODS

(c) Study population: The inclusion and exclusion criteria, setting and locations where data were collected.

(d) Participant recruitment: Was recruitment based on presenting symptoms, results from previous tests, or the fact that the participants had received the index tests or the reference standard?

(e) Participant sampling: Was the study population a consecutive series of participants defined by the selection criteria in item C and D? If not, specify how participants were further selected.

(f) Data collection: Was data collection planned before the index test and reference standard were performed (prospective study, 1 point) or after (retrospective study, 0 point)? TEST METHODS

(g) The reference standard and its rationale.

(h) Technical specifications of material and methods involved including how and when measurements were taken, and/or cite references for index tests and reference standard. Adequate biopsy fragment length? Liver biopsy processed according to standard pre-analytical/analytical procedures and scored following standard criteria?

(i) Definition of and rationale for the units, cut-offs, and/or categories of the results of the index tests and the reference standard.

(j) The number, training, and expertise of the persons executing and reading the index tests and the reference standard.

(k) Whether or not the readers of the index tests and reference standard were blinded to the results of the other test and describe any other clinical information available to the readers.

STATISTICAL METHODS

(l) Methods for calculating or comparing measures of diagnostic accuracy, and the statistical methods used to quantify uncertainty (e.g. 95% confidence intervals).

(m) Methods for calculating test reproducibility, if done.

RESULTS

(n) When study was performed, including beginning and end dates of recruitment.

(p) Clinical and demographic characteristics of the study population (at least information on age, gender, spectrum of presenting symptoms). Was the study population representative of the typical NAFLD spectrum?

(q) The number of participants satisfying the criteria for inclusion who did or did not undergo the index tests and/or the reference standard; describe why participants failed to undergo either test (a flow diagram is strongly recommended).

(r) Time-interval between the index tests and liver biopsy < 6 months, and any treatment administered in-between.

(s) Test results. Distribution of severity of disease (define criteria) in those with the target condition; other diagnoses in participants without the target condition.

(t) Test results. A cross-tabulation of the results of the index tests (including indeterminate and missing results) by the results of the reference standard; for continuous results, the distribution of the test results by the results of the reference standard.

(u) Test results. Any adverse events from performing the index tests or the reference standard.

(v) Estimates of diagnostic accuracy and measures of statistical uncertainty (e.g. 95% confidence intervals).

(w) How indeterminate results, missing data, and outliers of the index tests were handled.

(x) Estimates of variability of diagnostic accuracy between subgroups of participants, readers, or centers, if done.

(y) Estimates of test reproducibility, if done.

(z) Discussion. Discuss the clinical applicability of the study findings.

^a In the last column the score of the STARD check-list is reported, with the item(s) not satisfied by the study indicated in parentheses.

Sp = specificity; Se = sensitivity; FPHO = French public health organization; NR = not reported; TIMP-1 = tissue inhibitor of matrix metalloproteinase-1; PIIINP = N-terminal propeptide of type III collagen; HA = hyaluronic acid; CK-18 = cytokeratin-18; LSM = liver stiffness measurement; IQR/M = interquartile range/median ratio (reflects the variability of the validated measures; a ratio ≥ 30% is considered to indicate good accuracy and reproducibility of the measures).

Figure 6. Summary ROC curve and summary point of plasma CK-18 for diagnosing NASH (panel A), and of NAFLD fibrosis score (low cut-off, panel B), NAFLD (high cut-off, panel C), and Fibroscan (panel D) for advanced fibrosis.

Table III. Suggested assessment of patients with NAFLD for general physicians.

1) Evaluation of metabolic risk
Steps	Comments
Screen for obesity and metabolic syndrome (ATP III criteria) (Level I)	Measure BMI, waist circumference, blood pressure, fasting plasma glucose (FPG), lipid profile
75-g OGTT(Level II)	Perform in NAFLD patients without known diabetes according to standard guidelines (i.e. ADA) to classify glucose tolerance
Assess insulin resistance (Level II)	Measure fasting insulin together with FPG and calculate fasting indexes of insulin resistance (HOMA-IR, QUICKI), which is associated with the severity of liver disease and has prognostic value in NAFLD; OGTT-derived indexes for research purposes only
2) Evaluation of cardiovascular (CV) risk
Assess CV risk (Level I)	Measure total and HDL-C and calculate LDL-C if Tg < 200 mg/dL (2.26 mmol/L) and evaluate CVD risk scores (i.e. Framingham risk score (FRS)) (Level I). Perform B-mode carotid ultrasonography in patients without diabetes or established cardiovascular disease, at intermediate CVD risk (FRS 6%–20%) according to recent guidelines (Level II) (112)
3) Evaluation of liver-related risk
Assess the risk of progressive liver disease (Level I)	Simple steatosis (SS) has a benign hepatological prognosis and may be managed by general physicians with measures aiming at reducing overall cardio-metabolic risk. NASH, with or without advanced fibrosis, has a progressive course and poorer prognosis and mandates referral to hepatologist for tight follow-up and liver-specific experimental treatment
Non-invasively screen for advanced fibrosis (Level I)	Apply NAFLD fibrosis score and/or Fibroscan (non-obese subjects): if high probability of advanced fibrosis refer to gastroenterologist for liver biopsy and assessment of complications of cirrhosis (hepatic failure, portal hypertension, esophageal varices, HCC)
Assess risk of NASH by CK-18 measurement (Level II)	Refer to gastroenterologist for liver biopsy NAFLD patients with a high probability of NASH, with or without advanced fibrosis after non-invasive screening tests. Liver biopsy remains mandatory to stage and monitor the course of liver disease in NASH and if diagnosis of NAFLD is in doubt

ATP = Adult treatment panel; ADA = American Diabetes Association; OGTT = oral glucose tolerance test; C = cholesterol; Tg = triglyceride; HCC = hepatocellular carcinoma.

Figure 7. Proposed diagnostic algorithms combining non-invasive methods and liver biopsy. Algorithm 1 can be applied to any patient with NAFLD; algorithm 2 and 3 can be applied only to non-obese patients with NAFLD, since Fibroscan has limitations when BMI > 30 kg/m². In the third algorithm Fibroscan is applied to patients with a low and intermediate score for fibrosis, which implies a wider availability of Fibroscan, has the advantage of individuating all patients with advanced fibrosis, but it biopsies more patients with simple steatosis than the first model (13 versus 10 patients).

All models are conservative estimates of diagnostic performance of NAFLD fibrosis score and Fibroscan, i.e. they assume that all biopsied patients without advanced fibrosis in the first 2 steps have simple steatosis, not NASH, since the diagnostic performance of NAFLD fibrosis score and Fibroscan for detecting NASH is currently unknown.

Explanation: Based on our meta-analysis, three sequential diagnostic algorithms are proposed to individuate NASH, with and without advanced fibrosis; such algorithms require further prospective evaluation in large-scale multicenter cohort studies.

If a sample of 100 NAFLD subjects underwent a single LB, NASH would be found in 40 subjects, of which 20 would have advanced fibrosis (LB-to-all approach). In combined approaches, the first step should be the identification of patients with NASH and advanced fibrosis, requiring prompt gastroenterological referral. To this aim, NAFLD fibrosis score and Fibroscan may be combined with LB.

If we apply NAFLD fibrosis score to our NAFLD sample, this would direct 15 patients (13 with advanced fibrosis) to biopsy and leave 50 patients with a low probability and 35 patients with an indeterminate probability. If patients are obese, then the next step would target NASH with CK-18 measurement. At the end of diagnostic flow, 44 patients would be referred for LB (algorithm 1).

In non-obese subjects Fibroscan is reliable and accurate and may be applied after NAFLD fibrosis score to screen for advanced fibrosis patients with an indeterminate probability (algorithm 2) or with both an indeterminate and low probability (algorithm 3), depending on the local availability and experience.

At the end of the diagnostic flow, algorithm 2 would direct 45 patients (25 NASH, of whom 18 with advanced fibrosis) to LB; algorithm 3 would direct 49 patients (35 NASH, of whom 20 with advanced fibrosis) to LB.

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