The authors have declared that no competing interests exist.
Conceived and designed the experiments: GM. Performed the experiments: GM RG JHT ME SK MH RH HH SKY PC JG FB SH ESB MA LH XG SF AV YY KDL MC RH HV MML RR MC. Analyzed the data: GM RG JHT ME SK MH RH HH SKY PC JG FB SH ESB MA LH XG SF AV YY KDL MC RH HV SR GL LLW MM HCP KY MML RR MC. Contributed reagents/materials/analysis tools: GM RG JHT ME SK MH RH HH SKY PC JG FB SH ESB MA LH XG SF AV YY KDL MC RH HV SR GL LLW MM HCP KY MML RR MC. Wrote the first draft of the manuscript: GM. Contributed to the writing of the manuscript: GM.
In a systematic review and meta-analysis, Giovanni Musso and colleagues examine the association between non-alcoholic fatty liver disease and chronic kidney disease.
Chronic kidney disease (CKD) is a frequent, under-recognized condition and a risk factor for renal failure and cardiovascular disease. Increasing evidence connects non-alcoholic fatty liver disease (NAFLD) to CKD. We conducted a meta-analysis to determine whether the presence and severity of NAFLD are associated with the presence and severity of CKD.
English and non-English articles from international online databases from 1980 through January 31, 2014 were searched. Observational studies assessing NAFLD by histology, imaging, or biochemistry and defining CKD as either estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2 or proteinuria were included. Two reviewers extracted studies independently and in duplicate. Individual participant data (IPD) were solicited from all selected studies. Studies providing IPD were combined with studies providing only aggregate data with the two-stage method. Main outcomes were pooled using random-effects models. Sensitivity and subgroup analyses were used to explore sources of heterogeneity and the effect of potential confounders. The influences of age, whole-body/abdominal obesity, homeostasis model of insulin resistance (HOMA-IR), and duration of follow-up on effect estimates were assessed by meta-regression. Thirty-three studies (63,902 participants, 16 population-based and 17 hospital-based, 20 cross-sectional, and 13 longitudinal) were included. For 20 studies (61% of included studies, 11 cross-sectional and nine longitudinal, 29,282 participants), we obtained IPD. NAFLD was associated with an increased risk of prevalent (odds ratio [OR] 2.12, 95% CI 1.69–2.66) and incident (hazard ratio [HR] 1.79, 95% CI 1.65–1.95) CKD. Non-alcoholic steatohepatitis (NASH) was associated with a higher prevalence (OR 2.53, 95% CI 1.58–4.05) and incidence (HR 2.12, 95% CI 1.42–3.17) of CKD than simple steatosis. Advanced fibrosis was associated with a higher prevalence (OR 5.20, 95% CI 3.14–8.61) and incidence (HR 3.29, 95% CI 2.30–4.71) of CKD than non-advanced fibrosis. In all analyses, the magnitude and direction of effects remained unaffected by diabetes status, after adjustment for other risk factors, and in other subgroup and meta-regression analyses. In cross-sectional and longitudinal studies, the severity of NAFLD was positively associated with CKD stages. Limitations of analysis are the relatively small size of studies utilizing liver histology and the suboptimal sensitivity of ultrasound and biochemistry for NAFLD detection in population-based studies.
The presence and severity of NAFLD are associated with an increased risk and severity of CKD.
Chronic kidney disease (CKD)—the gradual loss of kidney function—is becoming increasingly common. In the US, for example, more than 10% of the adult population (about 26 million people) and more than 25% of individuals older than 65 years have CKD. Throughout life, the kidneys perform the essential task of filtering waste products (from the normal breakdown of tissues and from food) and excess water from the blood to make urine. CKD gradually destroys the kidneys' filtration units, the rate of blood filtration decreases, and dangerous amounts of waste products build up in the blood. Symptoms of CKD, which rarely occur until the disease is very advanced, include tiredness, swollen feet, and frequent urination, particularly at night. There is no cure for CKD, but progression of the disease can be slowed by controlling high blood pressure and diabetes (two risk factors for CKD), and by adopting a healthy lifestyle. The same interventions also reduce the chances of CKD developing in the first place.
CKD is associated with an increased risk of end-stage renal (kidney) disease and of cardiovascular disease. These life-threatening complications are potentially preventable through early identification and treatment of CKD. Because early recognition of CKD has the potential to reduce its health-related burden, the search is on for new modifiable risk factors for CKD. One possible new risk factor is non-alcoholic fatty liver disease (NAFLD), which, like CKD is becoming increasingly common. Healthy livers contain little or no fat but, in the US, 30% of the general adult population and up to 70% of patients who are obese or have diabetes have some degree of NAFLD, which ranges in severity from simple fatty liver (steatosis), through non-alcoholic steatohepatitis (NASH), to NASH with fibrosis (scarring of the liver) and finally cirrhosis (extensive scarring). In this systematic review and meta-analysis, the researchers investigate whether NAFLD is a risk factor for CKD by looking for an association between the two conditions. A systematic review identifies all the research on a given topic using predefined criteria, meta-analysis uses statistical methods to combine the results of several studies.
The researchers identified 33 studies that assessed NAFLD and CKD in nearly 64,000 participants, including 20 cross-sectional studies in which participants were assessed for NAFLD and CKD at a single time point and 13 longitudinal studies in which participants were assessed for NAFLD and then followed up to see whether they subsequently developed CKD. Meta-analysis of the data from the cross-sectional studies indicated that NAFLD was associated with a 2-fold increased risk of prevalent (pre-existing) CKD (an odds ratio [OR]of 2.12; an OR indicates the chance that an outcome will occur given a particular exposure, compared to the chance of the outcome occurring in the absence of that exposure). Meta-analysis of data from the longitudinal studies indicated that NAFLD was associated with a nearly 2-fold increased risk of incident (new) CKD (a hazard ratio [HR] of 1.79; an HR indicates often a particular event happens in one group compared to how often it happens in another group, over time). NASH was associated with a higher prevalence and incidence of CKD than simple steatosis. Similarly, advanced fibrosis was associated with a higher prevalence and incidence of CKD than non-advanced fibrosis.
These findings suggest that NAFLD is associated with an increased prevalence and incidence of CKD and that increased severity of liver disease is associated with an increased risk and severity of CKD. Because these associations persist after allowing for established risk factors for CKD, these findings identify NAFLD as an independent CKD risk factor. Certain aspects of the studies included in this meta-analysis (for example, only a few studies used biopsies to diagnose NAFLD; most used less sensitive tests that may have misclassified some individuals with NAFLD as normal) and the methods used in the meta-analysis may limit the accuracy of these findings. Nevertheless, these findings suggest that individuals with NAFLD should be screened for CKD even in the absence of other risk factors for the disease, and that better treatment of NAFLD may help to prevent CKD.
Please access these websites via the online version of this summary at
The
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The UK National Health Service Choices website provides information for patients on
The US National Kidney Foundation, a not-for-profit organization, provides information about
The not-for-profit
The British Liver Trust, a not-for-profit organization, provides information about
Chronic kidney disease (CKD) affects 4%–13% of the Western adult population and over 25% of individuals older than 65 years
Early recognition and treatment of CKD aimed at reducing renal disease progression and CVD complications may limit its health-related burden
The high morbidity, mortality, and health care costs associated with CKD have led investigators to seek novel modifiable risk factors. Non-alcoholic fatty liver disease (NAFLD), the hepatic manifestation of the metabolic syndrome, affects 30% of the general adult population and up to 60%–70% of diabetic and obese patients
We searched English and non-English language publications on MEDLINE, Ovid MEDLINE In-Process, EMBASE, ISI Web of Science, and Cochrane Library, and abstracts from the annual American Association for the Study of Liver Disease (AASLD), the American Gastroenterological Association (AGA), the European Association for the Study of the Liver (EASL), the Digestive Disease Week (DDW), and the American Society of Nephrology (ASN) Kidney Week meetings from 1980 through January 31, 2014. Search terms were: chronic kidney disease OR CKD OR kidney function OR kidney failure OR renal disease OR renal insufficiency OR renal failure OR glomerular filtration rate (GFR) OR estimated glomerular filtration rate (eGFR) OR creatinine OR albuminuria OR microalbuminuria OR macroalbuminuria OR proteinuria OR kidney injury AND NASH OR NAFLD OR non-alcoholic steatohepatitis OR non-alcoholic fatty liver disease OR fatty liver OR liver fat OR steatosis OR liver enzymes OR transaminase OR ALT OR AST OR GGT OR severity of liver disease OR fibrosis. A full list of the search strategies in different databases is reported in
Criteria were observational studies including adult (age ≥18 y) populations of any sex or ethnicity, with a diagnosis of NAFLD and CKD. NAFLD had to be diagnosed by (1) liver histology, (2) imaging (ultrasound, computer tomography, magnetic resonance imaging, or spectroscopy), or (3) biochemistry (elevations in serum AST, ALT, or GGT). Competing causes of steatosis, including alcohol consumption and viral hepatitis infection had to be excluded according to standard guidelines
Excluded from the meta-analysis were nnon-human studies, letters/case reports, studies including fewer than ten individuals, articles not reporting outcomes of interest or primary data (editorials, reviews), or using inadequate case definitions. In particular, studies were excluded that did not adequately consider competing causes of hepatic steatosis including alcohol, or viral hepatitis, or that enrolled a mixed population of cirrhotic and non-cirrhotic individuals (due to the potential confounding effects of cirrhosis
Primary outcome measures were differences in the prevalence or incidence of CKD. We compared the risk of primary outcomes between individuals with NAFLD and without NAFLD as well as across the main histological subtypes of NAFLD, since NASH and advanced fibrosis carry a significantly worse prognosis than steatosis and milder fibrosis stages, respectively
The severity of CKD was the secondary outcome measure. We estimated the effect of the severity of liver disease in NAFLD, as defined by NASH or advanced fibrosis, on the stage of CKD. CKD stage was categorized by GFR according to recent guidelines into CKD stage 3b (eGFR 30–44 ml/min/1.73 m2, CKD stage 4 (eGFR 15–29 ml/min/1.73 m2), and CKD stage 5 (eGFR<15 ml/min/1.73 m2)
Data were extracted from each study independently and in duplicate by two authors (GM, RG), using a predefined protocol (supplied in
Methodological quality of studies was assessed by the 22-item STROBE score
For all included studies, individual participant data (IPD) was solicited from principal investigators (PIs). PIs were asked to provide the most complete and updated data, even if the follow-up was longer than that used for their respective publications. The quality of the submitted IPD was assessed using pre-specified methods (see protocol in
Data not available upon database closure, either because the IPD had not been provided or because full manuscripts had not been published, were not included in our analyses.
For all analyses, we combined studies providing IPD and studies providing aggregate data (AD) into a pooled effect measure using the two-stage method
In reducing IPD to AD, for dichotomous outcomes we used multivariate logistic regression in cross-sectional studies to obtain log odds ratio (OR) with its standard error (SE), and Cox proportional hazard model in longitudinal studies (all providing time-to-event data) to obtain log hazard ratio (HR) and its SE separately for each study. We then combined individual ORs (for cross-sectional studies) and HRs (for longitudinal studies) and their 95% CIs from all included studies. Associations with continuous outcome variables were expressed as weighted mean differences (WMD) with 95% CI. Only the most adjusted risk estimates that were reported in the studies were included in the analysis. All measures of dispersion were converted to standard deviations (SDs).
The study-specific risk estimates were pooled using random-effects model, because this approach provides a more conservative assessment of the average effect size than fixed-effects model. Significance was set at
The I2 statistic and its 95% CI
We separately analyzed cross-sectional and longitudinal studies. Furthermore, for each outcome, the results of studies defining NAFLD by histology, imaging, or liver enzyme elevation are presented separately.
Sensitivity analysis was performed by repeating the meta-analysis after one study at a time was removed to assess whether any one study significantly affected pooled estimates. Additionally, a number of subgroup analyses were planned
We therefore explored whether differences in epidemiology of NAFLD and CKD between Asian and non-Asian populations affect the association of NAFLD with CKD studies including exclusively non-cirrhotic patients versus studies including exclusively cirrhotic patients; methods used to estimate GFR; outcomes related to CKD: studies assessing both eGFR and proteinuria versus studies assessing solely eGFR or proteinuria; study data availability: studies providing IPD versus studies providing exclusively AD.
When eight or more comparisons were available, the effect of continuous variables including age, whole-body and abdominal obesity (as estimated by BMI and by waist circumference, respectively)
Small study bias was examined by constructing funnel plots and by performing the Egger's test and the trim-and-fill analysis
Additionally, for the primary end-point we separately performed a one-stage meta-analysis of studies providing IPD, to examine how the association of NAFLD with CKD was altered when individual patient level covariates were accounted for
We used RevMan 5.2 (Nordic Cochrane Center) and SAS 9.2 (SAS Institute) for additional analyses that could not be done with RevMan. The trim-and-fill analysis was performed with Comprehensive Meta-analysis 2.0 (Biostat).
The mean (standard deviation [SD]) agreement between the two reviewers for study selection and for quality assessment were 0.89 (0.02) and 0.91 (0.04), respectively. The flow of study selection is reported in
STROBE score of included studies is provided as median (range).
Thirty-three studies (63,902 participants, 16 population-based and 17 hospital-based, 20 cross-sectional and 13 longitudinal) were included (
Author [REF] | Study characteristics |
CVD risk factors | Liver disease diagnosis and prevalence | CKD diagnosis and prevalence | Adjustments | Study Data [STROBE score] |
Campos |
Hospital; n = 197; mean age 43 y; male 16%; Asian 0% | Smokers 26%; DM 26%; HTN 56%; Mean BMI 48 kg/m2; Met Sy 24% | Histology; NAFLD 63%, NASH 32% | eGFR<60 ml/min/1.73 m2 (CKD-EPI); 10% | Age, gender, BMI, waist circumference, HTN, Met sy | IPD [22] |
Yilmaz |
Hospital; n = 87; mean age 47 y; male 55%; Asian 0% | Smokers 16%; DM 0%; HTN 30%; Mean BMI 30 kg/m2; Met Sy 37% | Histology; NAFLD 100%,NASH 67% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or AER 30–300 mg/d; 16% | Age, gender, BMI, waist circumference, BP, Tg, HDL-C, HOMA, smoking prediabetes, Met Sy | IPD [21(v)] |
Targher 2010 |
Hospital; n = 160; mean age 51 y; male 63%; Asian 0% | Smokers 21%; DM 6%; HTN 60%; Mean BMI 27 kg/m2; Met Sy 29% | Histology; NASH 100% | eGFR<60 ml/min/1.73 m2 (MDRD) or ACR≥30 mg/g; 14% | Age, gender, BMI, waist circumference, Tg, smoking, HOMA, Met Sy diabetes, BP | AD [22] |
Park 2011 |
Hospital; n = 562; mean age 53 y; male 68%; Asian 56% | Smokers 53%; DM 25%; HTN 30%; Mean BMI 30 kg/m2; Met Sy NA | All cirrhotic:12% NASH-related; 88% of other aetiologies;Matched for MELD and Child-Pugh score | eGFR<60 ml/min/1.73 m2 (MDRD); 17% | Obesity, DM, HTN, smoking, cardiovascular disease | IPD [21(s)] |
Yasui |
Hospital; n = 169; mean age 54 y; male 59%; Asian 100% | Smokers 23%; DM 31%; HTN 34%; Mean BMI 26 kg/m2; Met Sy 30% | Histology;NAFLD 100%, NASH 53% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or morning dipstick proteinuria ≥1+; 14% | BMI, HTN, waist circumference, low HDL-C, high Tg, smoking, DM | IPD [22] |
Musso |
Hospital; n = 80; mean age 48 y; male 67%; Asian 0% | Smokers 28%; DM 0%; HTN 52%; Mean BMI 25 kg/m2; Met Sy 31% | Histology;NAFLD 50%, NASH 25% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or AER≥30 mg/d; 20% | Age, gender, BMI, waist circumference, HTN, smoking, Met Sy | IPD [22] |
Francque |
Hospital; n = 230; mean age 48 y; male 37%; Asian 0% | Smokers 25%; DM 0%; HTN 50%; Mean BMI 39 kg/m2; Met Sy 47% | HistologyNAFLD 100%NASH 52% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or overt proteinuria (>300 mg/d); 9% | Age, BMI, HTN, waist circumference, smoking, Met Sy | IPD [22] |
Machado |
Hospital; n = 144; mean age 42 y; male 16%; Asian 0% | Smokers 28%; DM 26%; HTN 54%; Mean BMI 46 kg/m2; Met Sy 48% | HistologyNAFLD 100%NASH 25% | eGFR<60 ml/min/1.73 m2 (CKD-EPI); 6% | Age, AST, GGT, OSAS, BMI, waist circumference, HTN, smoking, Met Sy | IPD [22] |
Kim |
Hospital; n = 96; mean age 39 y; male 71%; Asian 100% | Smokers 31%; DM 0%; HTN 54%; Mean BMI 28.5 kg/m2; Met Sy 56% | HistologyNAFLD 100%NASH 56% | eGFR<60 ml/min/1.73 m2 (modified MDRD) or morning dipstick proteinuria ≥1+; 25% | Age, BMI, HTN waist circumference, smoking, Met Sy, dyslipidaemia | IPD [22] |
Targher Diabetologia |
Population; n = 2,103; mean age 61 y; male 62%; Asian 0% | Smokers 23%; DM 100%; HTN 66%; Mean BMI 27 kg/m2; Met Sy 52% | Ultrasound;67% | eGFR<60 ml/min/1.73 m2 (MDRD) or ACR≥30 mg/g; 13.5% | Age, gender, BMI, waist circumference, HTN, smoking, LDL.C, Tg, DM duration, HbA1c, medications, microalbuminuria, retinopathy | AD [22] |
Casoinic |
Hospital; n = 145; mean age 61 y; male 59%; Asian 0% | Smokers 28%; DM 100%; HTN 55%; Mean BMI 28 kg/m2; Met Sy 80% | Ultrasound;51% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or ACR 30–300 mg/g; 10% | Age, gender, C-reactive protein | AD [21(p)] |
Hwang |
Population; n = 1,361; mean age 48 y; male 71%; Asian 100% | Smokers 43%; DM 30%; HTN 15%; Mean BMI 25 kg/m2; Met Sy 21% | Ultrasound;43% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or ACR 30–300 mg/g; 16% | Age, gender, BMI, waist circumference, Tg, LDL-C, AST, ALT, GGT, HOMA,HTN, HbA1c, smoking, Met Sy | AD [22] |
Targher Diab Med |
Hospital; n = 343; mean age 44 y; male 45%; Asian 0% | Smokers 23%; DM 100%; HTN 43%; Mean BMI 24 kg/m2; Met Sy 46% | Ultrasound;53% | eGFR<60 ml/min/1.73 m2 (MDRD) or ACR≥30 mg/g; 40% | Age, gender, BMI, physical activity, family history of CVD, sys BP, Tg, HDL-C, smoking, DM duration, HbA1c, medications, microalbuminuria, eGFR | AD [22] |
Sirota |
Population; n = 11,469; mean age 42 y; male 45%; Asian 3.6% | Smokers 24%; DM 7%; HTN 25%; Mean BMI 25 kg/m2; Met Sy 28% | Ultrasound;36% | eGFR<60 ml/min/1.73 m2 (MDRD) or ACR≥30 mg/g; 25% | Age, gender, race, HTN, DM, sys BP, waist circumference, Tg, HDL-C, HOMA | AD [21(g)] |
Li |
Population; n = 1,412; mean age 43 y; male 64%; Asian 100% | Smokers 42%; DM 0%; HTN 17%; Mean BMI 24 kg/m2; Met Sy 11% | Ultrasound;33% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or morning dipstick proteinuria ≥1+; 5% | Age, gender, BMI, alcohol intake, smoking, sleep quality, physical activity, BP, Tg, cholesterol, Met Sy, AST, ALT | IPD [20(s, t)] |
Armstrong |
Population; n = 146; mean age 57 y; male 38%; Asian 5% | Smokers NA; DM 0%; HTN 36%; Mean BMI 28.8 kg/m2; Met Sy NA | Ultrasound;50% | eGFR<60 ml/min/1.73 m2 (CKD-EPI); 25% | BMI, HTN | IPD [22] |
Xia |
Population; n = 1,141; mean age 62 y; male 43%; Asian 100% | Smokers 15%; DM 19%; HTN 38%; Mean BMI 24 kg/m2; Met Sy 32% | Ultrasound;41% | eGFR<60 ml/min/1.73 m2 (MDRD) or ACR>30 mg/g; 12% | Age, BMI, smoking, HTN, Met Sy, uric acid | IPD [22] |
Ahn |
Populatiion; n = 1,706; mean age 58 y; male 55%; Asian 100% | Smokers 15%; DM 9%; HTN 38%; Mean BMI 24 kg/m2; Met Sy 26% | Ultrasound;32% | eGFR<60 ml/min/1.73 m2 (MDRD) or morning dipstick proteinuria ≥1+; 25% | Age, gender, BMI, smoking, waist circumference, AST, ALT, GGT, HTN, high TG, low HDL-C, DM | AD [21(v)] |
Anjaneya |
Hospital; n = 200; mean age 50 y; male 50%; Asian 100% | Smokers 17%; DM 0%; HTN 32%; Mean BMI 23 kg/m2; Met Sy 22% | Ultrasound;50% | eGFR<60 ml/min/1.73 m2 (MDRD) or AER 30–300 mg/d; 47% | No adjustment | AD [20(p, s)] |
Targher NMCD 2010 |
Population; n = 13,188; mean age 43 y; male 47%; Asian 4% | Smokers 24%; DM 8%; HTN 28%; Mean BMI 25 kg/m2; Met Sy 27% | Liver enzyme (GGT) elevation;10% | eGFR<60 ml/min/1.73 m2 (MDRD) or ACR≥30 mg/d; 14% | Age, gender, ethnicity, smoking, HTN, DM, lipid-lowering medications, BMI, waist circumference, fasting plasma glucose, total cholesterol, LDL-C, HDL-C, Tg, AST, ALT, alcohol intake, HOMA | AD [22] |
Studies with different definitions of NAFLD (histology, imaging, liver enzyme elevation) were analyzed separately and are grouped together.
Asian ethnicity was defined by birth within boundaries delineated West by the Red Sea, the Suez Canal, the Dardanelles strait, the Bosphorus the Caucasus and the Urals and East by the Bering Sea, the Japan and Indonesian archipelagos.
Modified 25-item
ACR, albumin-to-creatinine ratio; AER, albumin excretion rate; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BP, blood pressure; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; DM, diabetes mellitus; GGT, gamma-glutamyltransferase; HDL-C, high density lipoprotein cholesterol; HTN, hypertension; LDL-C, low density lipoprotein cholesterol; MELD, model for end-stage liver disease; Met Sy, metabolic syndrome; NA, not available; OSAS, obstructive sleep apnoea; Tg, triglycerides.
Author [REF] | Study characteristics |
Duration of follow-up | CVD risk factors | Liver disease diagnosis and prevalence | CKD diagnosis and prevalence | Adjustments | Study Data [STROBE score] |
Adams |
Hospital; n = 251; mean age 47 y; male 54%; Asian 3% | 14.2 years | Smokers 14%; DM 0%; HTN 26%; Mean BMI 33 kg/m2; Met Sy 36% | Ultrasound;Histology for 20% participants, NASH 56% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or ACR≥30 mg/d; 22% | Age, gender, BMI, HTN, smoking, Met Sy | IPD [22] |
Ekstedt |
Hospital; n = 63; mean age 47 y; male 73%; Asian 0% | 13.7 years | Smokers 17%; DM 0%; HTN 69%; Mean BMI 27 kg/m2; Met Sy 23% | Histology;NAFLD 100%NASH 51% | eGFR<60 ml/min/1.73 m2 (CKD-EPI); 19% | Age, BMI, HTN, high Tg, low HDL-C, Met Sy, use of statins, smoking | IPD [22] |
Soderberg |
Hospital; n = 125; mean age 45 y; male 72%; Asian 0% | 27.1 years | Smokers 34%; DM 24%; HTN 37%; Mean BMI 28 kg/m2; Met Sy 31% | HistologyNAFLD 67%NASH 33% | eGFR<60 ml/min/1.73 m2 (CKD-EPI); 27% | Age, BMI, HTN, smoking, DM, Met Sy | IPD [22] |
Wong |
Hospital; n = 51; mean age 44 y; male 65%; Asian 100% | 3.0 years | Smokers 14%; DM 50%; HTN 51%; Mean BMI 27 kg/m2; Met Sy 65 | Histology;NAFLD 100%NASH 33% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or ACR≥30 mg/g; 8% | Age, BMI, DM, HTN, waist circumference, Met Sy, smoking | IPD [22] |
Angulo |
Hospital; n = 191; mean age 51 y; male 35%; Asian 27% | 12.4 years | Smokers 23%; DM 17%; HTN 32%; Mean BMI 28 kg/m2; Met Sy 25% | Histology;NAFLD 100%NASH 46% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or morning dipstick proteinuria ≥1+; 18% | Age, BMI, DM, HTN, smoking, dyslipidaemia, Met Sy | IPD [22] |
Hamaguchi |
Population; n = 853; mean age 48 y; male 63%; Asian 100% | 5.0 years | Smokers 44%; DM 0%; HTN 9%; Mean BMI 22 kg/m2; Met Sy 11% | Ultrasound;20% | eGFR<60 ml/min/1.73 m2 (Japanese MDRD) or morning dipstick proteinuria ≥1+; 28% | Age, BMI, smoking, Met Sy, sys BP, LDL-C | IPD [22] |
Chang |
Population; n = 8,329; mean age 37 y; male 100%; Asian 100% | 3.2 years | Smokers 43%; DM 0%; HTN 0%; Mean BMI 24 kg/m2; Met Sy 6% | Ultrasound;30% | eGFR<60 ml/min/1.73 m2 (MDRD) or morning dipstick proteinuria ≥1+; 4% | Age, eGFR, HOMA, dyslipidaemia, BMI, C-reactive protein, Met Sy, sys BP | IPD [22] |
Targher JASN 2008 |
Population; n = 1,760; mean age 61 y; male 61%; Asian 0% | 6.5 years | Smokers 22%; DM 100%; HTN 65%; Mean BMI 26 kg/m2; Met Sy 55% | Ultrasound;73% | eGFR<60 ml/min/1.73 m2 (MDRD) or ACR≥300 mg/g; 31% | Age, gender, BMI, waist circumference, BP, LDL-C, Tg, smoking, DM duration, HbA1c, medications, microalbuminuria, baseline eGFR | AD [22] |
Lau |
Population; n = 2,858; mean age 48 y; male 46%; Asian 0% | 5.3 years | Smokers 28%; DM 8.9%; HTN 47%; Mean BMI 27 kg/m2; Met Sy 24% | Ultrasound;30% | eGFR<60 ml/min/1.73 m2 (CKD-EPI) or ACR≥30 mg/g; 8% | Age, BMI, Met Sy, HTN, dyslipidaemia, smoking | IPD [22] |
Athyros |
Population; n = 720; mean age 59 y; male 63%; Asian 0% | 3.0 years | Smokers 7%; DM 19%; HTN 44%; Mean BMI 26 kg/m2; Met Sy 31% | Ultrasound;29% | eGFR<60 ml/min/1.73 m2 (MDRD); 2% | No adjustments | AD [21(p)] |
El Azeem |
Population; n = 747; mean age 51 y; male 49%; Asian 0% | 3.0 years | Smokers 22%; DM 57%; HTN 32%; Mean BMI 34 kg/m2; Met Sy 67% | Ultrasound;35% | eGFR<60 ml/min/1.73 m2 (MDRD) or ACR≥30 mg/g; 29% | Age, BMI, HTN, dyslipidaemia, smoking, Met Sy | AD [22] |
Lee |
Population; n = 2,478; mean age 25 y; male 45%; Asian NA | 10 years | Smokers 27%; DM 1%; HTN 14%; Mean BMI 30 kg/m2; Met Sy NA | Liver enzyme (GGT) elevation;25% | ACR>25 mg/g; 10% | Age, gender, race, BMI, smoking, physical exercise, education, HDL-C, LDL-C, Tg | AD [20(s, t)] |
Ryu |
Population; n = 10,337; mean age 37 y; male 100%; Asian 100% | 3.5 years | Smokers 47%%; DM 0%; HTN 0%; Mean BMI 24 kg/m2; Met Sy 7% | Liver enzyme (GGT) elevation;24% | eGFR<60 ml/min/1.73 m2 (MDRD) or morning dipstick proteinuria ≥1+; 3.5% | Age, baseline eGFR, BMI, sys BP, fasting plasma glucose, total cholesterol, HDL-C, Tg, uric acid, HOMA, smoking, C-reactive protein, Met Sy, incident DM, incident HTN | IPD [21(v)] |
Studies with different definitions of NAFLD (histology, imaging, liver enzyme elevation) were analyzed separately and are grouped together.
Asian ethnicity was defined by birth within boundaries delineated West by the Red Sea, the Suez Canal, the Dardanelles strait, the Bosphorus the Caucasus and the Urals and East by the Bering Sea, the Japan and Indonesian archipelagos.
Modified 25-item
ACR, albumin-to-creatinine ratio; AER, albumin excretion rate; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BP, blood pressure; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; DM, diabetes mellitus; GGT, gamma-glutamyltransferase; HDL-C, high density lipoprotein cholesterol; HTN, hypertension; LDL-C, low density lipoprotein cholesterol; MELD, model for end-stage liver disease; Met Sy, metabolic syndrome; NA, not available; OSAS, obstructive sleep apnoea; Tg, triglycerides.
We obtained IPD for 20 studies (61% of included studies, 29,282 participants), including 11 cross-sectional studies (5,145 participants) (
NAFLD was defined by liver histology in 13 studies (2,205 participants)
Overall, the methodological quality of the studies was good: the median (range) STROBE score was 21 (20–22). Three studies did not report confounder-adjusted estimates and their precision (STROBE item [p])
Twelve studies enrolled exclusively non-diabetic individuals
Twenty-eight studies (85% of all studies, 97% of participants) adjusted for potential confounders, including all of the following: age, BMI, metabolic syndrome (overall and each component), hypertension, and smoking (
Outcome | Item Assessed in Analysis | Study Feature | Cross-sectional Studies | Longitudinal Studies |
OR (95% CI), I2 (95% CI), |
HR (95% CI), I2 (95%CI), |
|||
2.11 (1.82–2.44) I2 = 29% (21%–34%), |
1.79 (1.65–1.95), I2 = 0% (0%–18%), |
|||
1.04 (0.89–1.21), I2 = NA, |
No study | |||
2.09 (1.65–2.65), I2 = 78% (70%–83%), |
1.79 (1.65–1.95), I2 = 0% (0%–10%), |
|||
2.61(1.44–4.76) I2 = 0% (0%–21%), |
1.94 (0.53–7.16), I2 = NA, |
|||
2.09 (1.61–2.70), I2 = 79% (71%–85%), |
1.79(1.64–1.95), I2 = 0% (0%–13%), |
|||
2.26 (1.62–3.15), I2 = 0% (0%–12%), |
1.87 (1.31–2.67), I2 = NA, |
|||
2.14 (1.68–2.72), I2 = 78% (71%–84%), |
1.79 (1.64–1.95), I2 = 0% (0%–9%), |
|||
2.00 (1.22–3.26), I2 = NA, |
1.87 (1.31–2.67), I2 = NA, |
|||
2.21 (1.71–2.86), I2 = 78% (71%–84%), |
1.78 (1.62–1.95), I2 = 0% (0%–10%), |
|||
1.69 (1.34–2.12), I2 = NA, |
1.85 (1.50–2.28), I2 = NA, |
|||
2.37 (1.92–2.93), I2 = 23% (11%–31%), |
1.85 (1.22–2.28), I2 = 0% (0%–9%), |
|||
1.84 (1.43–2.37), I2 = 24% (18%–29%), |
1.67 (1.47–1.91), I2 = 0% (0%–11%), |
|||
2.06 (1.59–2.66), I2 = 81% (73%–87%), |
1.79 (1.64–1.95), I2 = 0% (0–10%), |
|||
2.45 (1.69–3.54), I2 = 0% (0%–10%), |
1.88 (1.33–2.65), I2 = 0% (0%–11%), |
|||
1.96 (1.49–2.59), I2 = 85% (77%–90%), |
1.78 (1.63–1.93), I2 = 0% (0%–11%), |
|||
2.37 (1.80–3.13), I2 = 0% (0%–14%), |
2.15 (1.49–3.15), I2 = 0% (0%–9%), |
|||
1.97 (1.71–2.27), I2 = 0% (0%–13%), |
1.70 (1.49–1.96), I2 = 0% (0%–10%), |
|||
2.32 (1.74–3.09), I2 = 61% (53%–68%), |
1.84 (1.65–2.06), I2 = 0% (0%–9%), |
|||
2.12 (1.67–2.69), I2 = 78% (66%–86%), |
1.79 (1.65–1.95), I2 = 0% (0%–18%, |
|||
2.20 (1.22–3.95), I2 = NA, |
No study | |||
1.80 (1.38–2.34), I2 = 83% (76%–88%), |
1.75 (1.58–1.95), I2 = 0% (0%–35%), |
|||
2.82 (2.15–3.69), I2 = 0% (0%–11%), |
1.99 (1.60–2.46), I2 = 0% (0%–10%), |
|||
2.08 (1.62–2.68), I2 = 81% (76%–85%), |
1.78 (1.63–1.95), I2 = 0% (0%–11%), |
|||
2.39 (1.55–3.68), I2 = 0% (0%–36%), |
1.95 (1.02–3.71), I2 = 0% (0%–11%), |
|||
No study | 1.87 (1.31–2.67), I2 = NA, |
|||
1.99 (1.56–2.52), I2 = 0% (0%–13%), |
1.89 (1.70–2.11), I2 = 0% (0%–8%) |
|||
2.14 (1.59–2.89) I2 = 76% (80%–90%), |
1.64 (1.43–1.88) I2 = 0% (0%–10%), |
|||
2.85 (1.72–4.72), I2 = 0% (0%–10%), |
2.12 (1.42–3.17), I2 = 0% (0%–19%), |
|||
1.22 (0.35–4.31), I2 = NA, |
No study | |||
2.26 (1.37–3.73), I2 = 0% (0%–13%), |
2.03 (1.30–3.17), I2 = 0% (0%–12%), |
|||
3.80 (1.47–9.81), I2 = 0% (0%–10%), |
2.54 (1.05–6.17), I2 = 0% (0%–10%), |
|||
2.53 (1.58–4.05), I2 = 0% (0%–14%), |
2.12 (1.42–3.17), I2 = 0% (0%–19%), |
|||
No study | No study | |||
No study | No study | |||
2.53 (1.58–4.05), I2 = 0% (0%–14%), |
2.12 (1.42–3.17), I2 = 0% (0%–19%), |
|||
2.53 (1.35–4.73), I2 = 0% (0%–12%), |
1.98 (1.28–3.06), I2 = 0% (0%–10%), |
|||
2.64 (1.05–6.62), I2 = 40% (37%–46%), |
3.08 (1.09–8.72), I2 = 0% (0%–9%), |
|||
2.53 (1.58–4.05), I2 = 0% (0%–14%), |
2.12 (1.42–3.17), I2 = 0% (0%–19%), |
|||
No study | No study | |||
No study | No study | |||
2.53 (1.58–4.05), I2 = 0% (0%–14%), |
2.12 (1.42–3.17), I2 = 0% (0%–19%), |
|||
2.37 (1.40–4.01), I2 = 0% (0%–13%), |
2.01 (1.16–3.48), I2 = 0% (0%–10%), |
|||
3.25 (1.18–8.98), I2 = 0% (0%–19%), |
2.26 (1.26–4.05), I2 = 0% (0%–12%), |
|||
No study | No study | |||
2.53 (1.58–4.05), I2 = 0% (0%–14%), |
2.12 (1.42–3.17), I2 = 0% (0%–19%), |
|||
No study | No study | |||
4.97 (2.89–8.55), I2 = 0% (0%–12%), |
3.29 (2.30–4.71), I2 = 0% (0%–18%), |
|||
6.94 (1.73–17.76), I2 = NA, |
No study | |||
5.84(3.25–10.49), I2 = 0% (0%–12%), |
2.82 (1.86–4.28), I2 = 0% (0%–11%), |
|||
5.01 (1.46–17.21), I2 = 0% (0%–13%), |
4.19 (2.10–8.38), I2 = 0% (0%–11%), |
|||
5.20 (3.14–8.61), I2 = 0% (0%–17%), |
3.29 (2.30–4.71), I2 = 0% (0%–18%), |
|||
No study | No study | |||
No study | No study | |||
5.20 (3.14–8.61), I2 = 0% (0%–17%), |
3.29 (2.30–4.71), I2 = 0% (0%–18%), |
|||
6.00 (3.15–11.43), I2 = 0% (0%–10%), |
2.86 (1.93–4.22), I2 = 0% (0%–11%), |
|||
4.15 (1.85–9.32), I2 = 0% (0%–9%), |
6.01 (2.25–16.09), I2 = 34% (27%–39%), |
|||
5.20 (3.14–8.61), I2 = 0% (0%–17%), |
3.29 (2.30–4.71), I2 = 0% (0%–18%), |
|||
No study | No study | |||
4.07 (1.52–10.09), I2 = 0% (0%–11%), |
No study | |||
5.67 (3.15–10.20), I2 = 0% (0%–19%), |
3.29 (2.30–4.71), I2 = 0% (0%–18%), |
|||
5.05 (2.95–8.66), I2 = 0% (0%–9%), |
3.56 (2.05–6.17), I2 = 16% (10%–21%), |
|||
6.36 (1.50–26.91), I2 = 0% (0%–16%), |
3.11 (1.85–5.22), I2 = 0% (0%–11%), |
|||
No study | No study | |||
5.28 (3.06–9.12), I2 = 0% (0%–10%) |
3.29 (2.30–4.71), I2 = 0% (0%–18%), |
|||
4.71 (1.25–17.72), I2 = NA, |
No study |
Subgroup analysis was planned a priori to assess the impact of the following items on the association between NAFLD and CKD: (1) Fulfilment of STROBE items: we planned to repeat the analysis after excluding studies not fulfilling each STROBE item (different STROBE items are described in footnote to
Eleven studies enrolled exclusively Asian populations
All studies included non-cirrhotic participants, except one cross-sectional study comparing NASH-related cirrhosis with cirrhosis of other aetiologies, matched for Child-Pugh and Model for End-stage Liver Disease-(MELD) scores
GFR was estimated with the CKD-EPI equation in 16 studies
In cross-sectional studies, pooled OR for the presence of CKD of NAFLD versus non-NAFLD was 2.12 (95% CI 1.69–2.66, I2 = 77% [95% CI 66%–84%], N-comparisons = 17,
NAFLD versus non-NAFLD, outcome: prevalent chronic kidney disease in cross-sectional studies. Studies assessing NAFLD by imaging, histology or liver enzyme elevation were considered separately.
In longitudinal studies, pooled HR for incident CKD of NAFLD versus non-NAFLD was 1.79 (95% CI 1.65–1.95, I2 = 0% [95% CI 0%–18%],
NAFLD versus non-NAFLD, outcome: incident chronic kidney disease in prospective studies. NAFLD was defined by imaging, histology, or liver enzyme elevation. Studies assessing NAFLD by imaging, histology, or liver enzyme elevation were considered separately.
In both cross-sectional and longitudinal studies, the difference between NAFLD and non-NAFLD patients remained statistically significant even when considering eGFR as a continuous variable or when considering only proteinuria as outcome (Figures S2A, S2B, S3A, and S3B within
In both cross-sectional and longitudinal studies, meta-regression analysis found no association between CKD and age (cross-sectional studies: β = 0.004, 95% CI −0.023 to 0.031,
The Egger's test found no strong evidence for small study bias and the trim-and-fill analysis did not appreciably attenuate the strength of the association (Figures S4A and S4B within
In cross-sectional studies, pooled OR for CKD of NASH versus steatosis was 2.53 (95% CI 1.58–4.05, I2 = 0% [95% CI 0%–14%],
(A) NASH versus simple steatosis in biopsy-proven non-cirrhotic NAFLD; outcome: prevalent chronic kidney disease in cross-sectional studies. (B) Advanced (stage F3) fibrosis versus no-advanced (stage F0–F2) fibrosis in biopsy-proven non-cirrhotic NAFLD, outcome: prevalent CKD in cross-sectional studies.
NASH and advanced fibrosis were also associated with higher ORs for proteinuria and with a lower eGFR than steatosis and non-advanced fibrosis, respectively (Figures S5A, S5B, S6A, and S6B within
Meta-regression analysis found no association between CKD and age (for NASH: β = 0.050, 95% CI −0.039 to 0.140,
The Egger's test found no strong evidence for small study bias and the trim-and-fill analysis did not appreciably attenuate the strength of the association (Figures S4A–S4D within
In longitudinal studies, pooled HR for incident CKD of NASH versus simple steatosis was 2.12 (95% CI 1.42–3.17, I2 = 0% [95% CI 0%–19%,
(A) NASH versus simple steatosis in biopsy-proven noncirrhotic NAFLD; outcome: incident CKD in prospective studies. (B) Advanced (stage F3) fibrosis versus no-advanced (stage F0–F2) fibrosis in biopsy-proven non-cirrhotic NAFLD, outcome: incident CKD in prospective studies.
NASH and advanced fibrosis were also associated with a higher OR for incident proteinuria and with more severe eGFR reduction than steatosis and non-advanced fibrosis, respectively (Figures S7A, S7B, S8A, and S8B within
Meta-regression analysis found no association between CKD and age (for NASH: β = −0.019, 95% CI −0.113 to 0.774,
The Egger's test found no strong evidence for small study bias and the trim-and-fill analysis did not appreciably attenuate the strength of the association (Figures S4E and S4F within
In cross-sectional studies, pooled OR for CKD stage 3b of NASH versus steatosis was 3.38 (95% CI 1.11–10.31, I2 = 0% [95% CI 0%–17%],
The presence of serum creatinine elevation, configuring severely decreased renal function (CKD stage 4) or renal failure (CKD stage 5), was an exclusion criterion in cross-sectional studies, which focused on the association of NAFLD with clinically unrecognized (stage 1–3) CKD.
In longitudinal studies, pooled HR for CKD stage 3b, 4, and 5 (renal failure) was significantly higher in NASH versus steatosis: OR for CKD stage 3b: 2.49 (95% CI 1.21–5.13, I2 = 0% [95% CI 0%–21%], n-comparisons = 7,
(A) NASH versus simple steatosis in biopsy-proven non-cirrhotic NAFLD; outcome: incident CKD stage 3b in prospective studies. (B) NASH versus simple steatosis in biopsy-proven non-cirrhotic NAFLD; outcome: incident CKD stage 4 in prospective studies.
(A) NASH versus simple steatosis in biopsy-proven non-cirrhotic NAFLD; outcome: incident CKD stage 5 (renal failure) in prospective studies. (B) Advanced (stage F3) fibrosis versus no advanced (stage F0–F2) fibrosis in biopsy-proven non-cirrhotic NAFLD; outcome: incident CKD stage 3b in prospective studies.
Similarly, pooled HR for CKD stage 3b, 4, and 5 (renal failure) was significantly higher in advanced versus non-advanced fibrosis: OR for CKD stage 3b: 7.48 (95% CI 2.95–18.97, I2 = 23% [95% CI 0%–35%], n-comparisons = 7,
(A) advanced (stage F3) fibrosis versus no advanced (stage F0–F2) fibrosis in biopsy-proven non-cirrhotic NAFLD; outcome: incident CKD stage 4 in prospective studies. (B) Advanced (stage F3) fibrosis versus no advanced (stage F0–F2) fibrosis in biopsy-proven non-cirrhotic NAFLD; outcome: incident (CKD) stage 5 (renal failure) in prospective studies.
There was no heterogeneity in the meta-analysis of overall events, suggesting a consistent disease effect.
The magnitude and direction of the associations were unaltered across studies fulfilling different STROBE score items in non-diabetic individuals (Figures S10–S14 and S23–S26 within
The magnitude and direction of the effect were unaltered across studies fulfilling different STROBE score items (Figures S34 and S38 within
All studies adjusted for traditional risk factors for CKD, were hospital-based and enrolled non-cirrhotic patients.
The magnitude and direction of the effect remained unaltered in non-diabetic versus diabetic individuals (Figures S44 and S47 within
Twenty studies (29,282 participants, 11 cross-sectional studies, nine longitudinal studies) were included in this analysis. We first analyzed the influence of each single pre-specified individual patient level covariate on the association of NAFLD with CKD with NAFLD and covariate as fixed-effect and the study as random-effects. In a second step, we did a complete case multivariable analysis with respect to NAFLD and all pre-specified covariates. The covariates entered in the models were age, BMI, metabolic syndrome (presence versus absence), diabetes (presence versus absence), hypertension (presence versus absence), smoking status (current smokers versus non-smokers), ethnicity (Asian versus non-Asian population), cirrhosis (presence versus absence), waist circumference, HOMA-index, duration of follow-up (for longitudinal studies).
The magnitude of the effect of NAFLD on CKD remained largely unaffected after adjusting for the covariates separately and in the fully adjusted models (
Outcome | Cross-sectional Studies | Longitudinal Studies | ||||
Covariate | Participants | OR (95% CI) | Participants | HR (95% CI) | ||
507 diabetic 3,031 non-diabetic | 2.62 (1.95–3.57) | 0.00001 | 2,046 diabetic, 39,365 non-diabetic | 1.99 (1.83–2.25) | 0.00001 | |
51 (18–89) | 2.39 (1.93–2.99) | 0.00003 | 38 (20–80) | 2.10 (1.74–2.63) | 0.000006 | |
26 (16–71) | 2.21 (1.85–2.67) | 0.00006 | 24 (16–47) | 2.40 (1.94–2.99) | 0.00001 | |
885 with Met Sy, 2,654 without Met Sy | 2.43 (1.95–3.06) | 0.00001 | 3,758 with Met Sy, 19,226 without Met Sy | 2.13 (1.77–2.71) | 0.00009 | |
1,061 with HTN, 2,477 without HTN) | 2.19 (1.78–2.61) | 0.00002 | 4,367 with HTN, 18,617 without HTN | 2.01 (1.69–2.77) | 0.00002 | |
1,168 smokers, 2,370 non-smokers | 2.11 (1.75–2.58) | 0.00005 | 9,653 smokers, 13,331 non-smokers | 2.28 (1.87–2.89) | 0.00008 | |
2,867 Asians, 1,071 non-Asian | 2.25 (1.87–2.70) | 0.00002 | 18,519 Asians, 6,937 non-Asians | 2.32 (1.32–4.10) | 0.00004 | |
2,976 non-cirrhotic, 562 cirrhotic |
2.34 (1.92–2.85) | 0.00001 | No cirrhotic participant | |||
91 (54–153) | 2.63 (2.02–3.12) | 0.00007 | 89 (51–150) | 2.52 (2.00–3.02) | 0.00007 | |
1.8 (1.0–15.1) | 2.55 (2.00–2.98) | 0.00001 | 1.6 (0.6–10.3) | 2.12 (1.76–2.54) | 0.0009 | |
5 (1–29) | 2.48 (1.98–2.97) | 0.0001 | ||||
3,538 individuals | 1.95 (1.55–2.71) | 0.00001 | 22,984 individuals | 1.91 (1.68–2.21 | 0.00001 | |
119 diabetic, 769 non-diabetic | 2.77 (1.81–4.24) | 0.0004 | 96 diabetic, 333 non-diabetic | 2.32 (1.55–3.48) | 0.00009 | |
46 (18–80) | 3.01 (2.50–3.72) | 0.00001 | 47 (18–67) | 2.61 (1.71–3.24) | 0.00004 | |
33 (18–59) | 2.78 (2.09–3.24) | 0.0003 | 27 (18–47) | 2.32 (1.59–3.15) | 0.00006 | |
355 with Mey Sy, 532 without Met Sy | 2.66 (2.05–3.12) | 0.00008 | 136 with Met Sy, 293 without Met Sy | 2.71 (2.18–3.59) | 0.0001 | |
408 with HTN, 479 without HTN | 2.59 (1.97–3.18) | 0.00002 | 172 with HTN, 257 without HTN | 2.58 (1.99–3.11) | 0.00007 | |
222 smokers, 665 non-smokers | 2.69 (1.63–3.82) | 0.00001 | 103 smokers, 326 non-smokers | 2.56 (1.54–3.13) | 0.0002 | |
263 Asians, 624 non-Asians | 3.14 (2.44–4.96) | 0.00001 | 102 Asians, 327 non-Asians | 2.37 (1.41–3.98) | 0.0001 | |
No cirrhotic participant | — | — | No cirrhotic participant | — | — | |
110 (51–162) | 2.62 (2.01–3.31) | 0.0006 | 99 (70–115) | 2.76 (2.01–3.48) | 0.0003 | |
3.1 (0.3–28.1) | 2.54 (1.98–2.96) | 0.00002 | 2.7 (0.8–3.9) | 2.24 (1.55–2.46) | 0.00001 | |
— | — | — | 14 (3–30) | 2.31 (1.42–2.87) | 0.00007 | |
887 individuals | 2.42 (1.80–3.84) | 0.0001 | 429 individuals | 2.01 (1.40–2.98) | 0.0001 | |
119 diabetic, 769 non-diabetic | 5.39 (3.66–8.20) | 0.0001 | 96 diabetic, 333 non-diabetic | 3.88 (2.97–5.23) | 0.00001 | |
46 (18–80) | 5.12 (3.71–6.99) | 0.00009 | 47 (18–67) | 3.91 (2.56–4.98) | 0.00007 | |
33 (18–59) | 4.89 (3.94–5.98) | 0.00001 | 27 (18–47) | 3.45 (2.51–4.02) | 0.00004 | |
355 with Mey Sy, 532 without Met Sy | 5.00 (4.11–5.97) | 0.00007 | 136 with Met Sy, 293 without Met Sy | 3.28 (2.39–4.52) | 0.00006 | |
408 with HTN, 479 without HTN | 4.98 (3.81–6.02) | 0.0003 | 172 with HTN, 257 without HTN | 4.11 (2.88–5.51) | 0.0001 | |
222 smokers, 665 non-smokers | 5.27 (3.94–6.42) | 0.00008 | 103 smokers, 326 non-smokers | 3.29 (2.24–4.43) | 0.00002 | |
263 Asians, 624 non-Asians | 5.18 (3.57–6.82) | 0.00001 | 102 Asians, 327 non-Asians | 3.70 (1.46–9.39) | 0.0006 | |
No cirrhotic participant | — | — | No cirrhotic participant | — | — | |
110 (51–162) | 5.39 (4.21–6.13) | 0.0006 | 99 (70–115) | 3.39 (2.12–4.51) | 0.00001 | |
3.1 (0.3–28.1) | 5.13 (4.01–6.29) | 0.00004 | 2.7 (0.8–3.9) | 3.70 (2.12–4.91) | 0.00003 | |
— | — | — | 14 (3–30) | 3.58 (2.46–5.89) | 0.0009 | |
887 individuals | 4.86 (3.54–6.69) | 0.00001 | 429 participants | 3.00 (2.08–4.33) | 0.0001 |
Data from all studies providing IPD were pooled together into a single dataset and effect estimates were calculated using multivariate logistic regression (cross-sectional studies) or Cox proportional hazard models (longitudinal studies). In these models, studies were incorporated as cluster and treated as random-effect, while covariates were treated as fixed-effect. The individual patient covariates entered in the models were: age, BMI, metabolic syndrome, hypertension, smoking status, diabetes, ethnicity (Asian versus non-Asian population), presence of cirrhosis, waist circumference, HOMA-IR index, duration of follow-up (for longitudinal studies). Finally, a fully adjusted model was run, with all covariates entered.
HTN, hypertension; Met Sy, metabolic syndrome; waist, waist circumference.
For continuous variables, median (range) of values is reported.
All cirrhotic individuals derive from the study by Park et al.
The main results of our analysis are the following: (1) NAFLD was associated with an increased prevalence and incidence of CKD; (2) liver disease severity in NAFLD was associated with an increased risk and severity of CKD; (3) these associations remained statistically significant in diabetic and non-diabetic individuals, as well as in studies adjusting for traditional risk factors for CKD, and were independent of whole body/abdominal obesity and insulin resistance.
The prevalence of CKD is rapidly growing and in the United States over 1.1 million individuals are estimated to have ESRD by the year 2015
NAFLD is an emerging risk factor for end-stage liver disease and CVD: the frequency of NASH as the primary indication for liver transplantation has increased from 1.2% to 9.7% in the last decade, becoming the third most common indication for liver transplantation in the United States
Current guidelines do not recommend screening for CKD in the absence of traditional risk factors for CKD
From a therapeutic standpoint, there is a considerable potential for improving the current care of NAFLD patients with CKD: with respect to lifestyle interventions, smoking cessation should be more vigorously pursued, as cigarette smoking is an established risk factor for CKD, and may also aggravate NAFLD
Further research is required to unravel the specific cascades linking NAFLD and kidney disease. NAFLD and CKD share common risk factors and therefore both liver and kidney injury may be driven by obesity-associated mechanisms of disease, including lipotoxicity, oxidative stress, enhanced pro-inflammatory cytokine, and renin-angiotensin-aldosterone system (RAAS) axis activation
We also found a cross-sectional association between NAFLD and CKD, implying that even mild renal dysfunction may promote liver disease, in a mutual negative loop with detrimental cardio-metabolic consequences Consistently, uni-nephrectomized rats developed body fat redistribution from adipose depots to non-adipose tissues, profound dysregulation of hepatic fatty acid metabolism, steatohepatitis, insulin resistance, hyperglycaemia, and dyslipidaemia early after uni-nephrectomy and long before glomerulosclerosis and chronic renal failure occur
Lastly, since NAFLD is an emerging risk factor for liver-related and cardiovascular complications, the hypothesis that the presence of CKD may represent a simple, cost-effective tool to predict increased liver-related and CVD risk in NAFLD is intriguing and warrants assessment in large-scale prospective studies. Currently, in fact, there is no validated non-invasive marker to predict the risk of both liver disease progression and future CVD, the main causes of death in NAFLD patients
Our findings may also have therapeutic research implications. Future trials will need to evaluate the impact of experimental treatments on kidney-related outcomes. Notably, only two of the available randomized controlled trials in NAFLD reports the effect of drugs on eGFR and proteinuria and none has adequate size and duration to evaluate the impact of treatments on kidney-related clinical outcomes.
Our analysis has limitations, which are intrinsic to the nature of included studies and provide the basis for future research. Studies with biopsy-proven NAFLD were by their own nature less numerous and smaller than those adopting ultrasonographic/biochemical definitions of NAFLD, leaving the possibility of small study bias that is not detected by current tests. Furthermore, these studies were performed in tertiary centers with the possibility of selection bias. Conversely, ultrasound/liver enzyme elevations are relatively insensitive to detect NAFLD, with possible misclassification of individuals with NASH/advanced fibrosis as healthy controls and underestimation of the strength of the association between NAFLD and CKD. However, there was no heterogeneity between studies: disease effect on CKD.
Finally, despite our best efforts IPD were unavailable from as much as 39% of relevant studies, representing 54% of participant population. While meta-analysis of AD has several limitations, including ecological bias and study-level confounding, excluding such a substantial proportion of relevant literature from our analysis would have raised the concern of data availability bias
Balancing all these reasons, we presented all relevant literature evidence by combining IPD with AD in the main analysis, and separately analyzed studies providing IPD with a one-stage method: notably, the two analyses yielded similar results, further supporting the robustness of overall findings.
In conclusion, our analysis shows that the presence and severity of NAFLD are associated with an increased risk and severity of CKD and may be a target for the prevention and treatment of CKD. Future research should evaluate strategies and interventions to prevent renal disease progression in individuals with NAFLD.
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We are deeply grateful to the following authors, who, by providing without any compensation further details on their published studies, made this analysis possible:
aggregate data
body mass index
chronic kidney disease
cardiovascular disease
end-stage renal disease
glomerular filtration rate
estimated glomerular filtration rate
homeostasis model assessment of insulin resistance
hazard ratio
individual participant data
Modified Diet in Renal Disease
non-alcoholic fatty liver disease
non-alcoholic steatohepatitis
odds ratio