Diabetes mellitus (DM) is a worldwide public health challenge. WHO estimated that there were around 422 million people living with diabetes and that there was a rising trend in the number of people living with DM [
In China, with the rapid economic growth and urbanization, lifestyle changed significantly. At the same time, the prevalence of T2DM has been increasing dramatically. IDF Diabetes Atlas estimated that in 2017, the prevalence of diabetes was 10.9%, and it estimated that there were 114 million people living with diabetes and 61 million people with undiagnosed diabetes [
Although the DN epidemic in China is striking [
This meta-analysis was conducted according to the PRISMA guideline. The PubMed, Embase, Google Scholar, Chinese Wanfang databases, and Chinese National Knowledge Infrastructure (CNKI) were searched. We used the following search terms: (“nephropathy” OR “kidney diseases”) AND (“diabetes mellitus” OR “diabetes” OR “mellitus”) AND (“epidemiology” OR “prevalence”). We searched for studies published from January 1980 to October 2019 to identify relevant articles. The literature was limited to those published in Chinese and English as both reviewers are fluent in these languages.
Diabetes is a disease that blood glucose levels rise higher than normal and for extended periods. T2DM is the most common form of diabetes [
Two investigators (KY and XXZ) independently reviewed the search results and selected articles to determine eligibility and to extract study data. Disagreements of data extraction among two reviewers were reconciled by discussion. Standardized Excel spreadsheet abstraction forms were designed to capture all relevant information required for analyses, including first author, date of publication, diagnosis standard for DN, diagnosis standard for DM, study location, population source, urban/rural, age of subjects, BMI, sex, duration of DM (years), systolic pressure, diastolic pressure, number of patients with DM and DN, and quality score.
Methodological quality assessments were conducted using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist of observational studies [
The pooled prevalence of DN was calculated using the inverse variance method, as previously described. Briefly, if the tests met the hypothesis of homogeneity, fixed effects models were used; otherwise, random effects models were used [
The leave-one-out sensitivity test was used to confirm that our findings were not driven by any single study. In addition, Egger’s tests were used to detect potential publication bias by examining the funnel plot symmetry.
A total of 7161 citations were retrieved in the literature search. Of these, 7075 were excluded after screening titles and abstracts, and 86 were selected for further evaluation. Finally, 30 articles that provided the rates of DN in adults with T2DM were included in this review (Figure
Flow diagram of studies included in meta-analysis.
A descriptive summary of the included studies is provided in Table
Summarized information of studies included in meta-analysis.
First author (publication year) | Survey date | Diagnosis standards for DN | Diagnosis standards for DM | Area | Population source | Age (years) | BMI | Sex (%males) | Course of DM (years) | Systolic blood pressure | Diastolic blood pressure | Sample size | Quality score |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Song (2014) [ |
2012 | Clinical diagnosis | Clinical diagnosis | Shanghai | Community-based | <59 | 25 | 55.7 | NA | NA | NA | 436 | 12 |
Xu (2012) [ |
2008-2009 | KDOQI 2007 | ADA criteria 2005 | Shanghai | Community-based | NA | 40.8 | NA | NA | 1421 | 22 | ||
Ke (2013) [ |
2011 | Mogensen criteria | WHO criteria 1999 | Huangshi, Hubei | Community-based | NA | NA | 53.1 | NA | NA | NA | 918 | 20 |
Guo (2006) [ |
2002 | Clinical diagnosis | WHO criteria 1999 | Beijing | Hospital-based | NA | 60 | NA | NA | NA | 402 | 22 | |
Mou (2010) [ |
2003-2008 | Renal biopsy | History of DM | Shanghai | Hospital-based | NA | 52.2 | NA | 69 | 22 | |||
Kung (2014) [ |
2009-2011 | Clinical diagnosis | Clinical diagnosis | Hong Kong | Hospital-based | 60(20-) | 25 | 48.8 | 15856 | 22 | |||
Zhou (2012) [ |
2003-2010 | Clinical diagnosis | WHO criteria 1999 | Beijing | Hospital-based | 25 | 56.8 | NA | NA | 1758 | 21 | ||
Qu (2003) [ |
1994-2001 | Mogensen criteria | 1985 WHO/1999 China criteria | Changsha, Hunan | Hospital-based | NA | 47.4 | NA | NA | 1718 | 17 | ||
Teng (2001) [ |
1997-2000 | Clinical diagnosis | WHO criteria 1985 | Shanghai | Hospital-based | >43 | NA | NA | NA | NA | NA | 1059 | 22 |
Xing (2009) [ |
2007-2009 | Clinical diagnosis | WHO criteria 1999 | Benxi, Liaoning | Hospital-based | NA | 51.2 | NA | NA | 2276 | 22 | ||
Lu (2002) [ |
1996-2001 | Clinical diagnosis | Clinical diagnosis | Suzhou, Jiangsu | Hospital-based | >45 | NA | NA | NA | NA | 821 | 21 | |
Liu (2010) [ |
2003-2006 | Renal biopsy | Clinical diagnosis | Shanghai | Hospital-based | NA | 56.5 | 6 | NA | NA | 46 | 21 | |
Zou (2000) [ |
1993-1998 | Clinical diagnosis | WHO criteria 1985 | Beijing | Hospital-based | NA | 62.8 | NA | NA | 1217 | 20 | ||
Tang (2005) [ |
NA | Clinical diagnosis | History of DM | Panzhihua, Sichuan | Hospital-based | 17-83 | NA | 52.5 | 0-20 | NA | NA | 324 | 18 |
Yu (2006) [ |
1991-2000 | Clinical diagnosis | History of DM | Hangzhou, Zhejiang | Hospital-based | NA | 49.3 | NA | NA | 874 | 19 | ||
Wang (2014) [ |
2013 | Clinical diagnosis | History of DM | Fushun, Liaoning | Hospital-based | 45.1 | NA | NA | 750 | 20 | |||
Chen (2007) [ |
2005 | ADA criteria 1997 | Clinical diagnosis | Shanghai | Hospital-based | 48.1 | 408 | 20 | |||||
Yu (2012) [ |
2011 | Clinical diagnosis | WHO criteria 1999 | Shanghai | Community-based | 40.5 | NA | 516 | 21 | ||||
Xu (2016) [ |
2014-2015 | CTM criteria 2010 | WHO criteria 2004 | Linyi, Shandong | Hospital-based | NA | NA | NA | NA | NA | 500 | 19 | |
Zhang (2016) [ |
2011 | ADA criteria 2007 | WHO criteria 1999 | Dalian, Liaoning | Hospital-based | 61.5 | 25.7 | NA | NA | 152.9 | 83.23 | 2345 | 20 |
Li (2014)-1 [ |
2009 | Clinical diagnosis | Clinical diagnosis | Tongxiang, Zhejiang | Hospital-based | >60 | NA | 57.7 | NA | NA | NA | 302 | 20 |
Li (2014)-2 [ |
2012 | Clinical diagnosis | Clinical diagnosis | Tongxiang, Zhejiang | Hospital-based | >60 | NA | 57.7 | NA | NA | NA | 494 | 20 |
Zeng (2014) [ |
2010-2013 | Clinical diagnosis | ADA criteria 2009 | Guangzhou, Guangdong | Hospital-based | NA | 56.7 | 1~24 | NA | NA | 842 | 21 | |
Hu (2016) [ |
2011-2012 | Clinical diagnosis | WHO criteria 1999 | Guangdong | Hospital-based | NA | 48.8 | NA | NA | NA | 4101 | 23 | |
Wang (2017) [ |
2014-2015 | KDOQI 2014 | History of DM | Lanzhou, Gansu | Hospital-based | NA | 58.6 | NA | NA | 558 | 21 | ||
Guo (2016) [ |
2005-2012 | KDOQI 2012 | WHO criteria 1999 | Shanghai | Hospital-based | 55.1 | 8.48 | 3301 | 22 | ||||
Zhuo (2013) [ |
2003-2011 | Renal biopsy | ADA criteria 2007 | Beijing | Hospital-based | 28-64 | NA | 61.9 | 2-20 | NA | NA | 244 | 22 |
Yang (2018) [ |
2014-2017 | KDIGO guidelines 2012 | History of DM | Hong Kong | Hospital-based | NA | 50.4 | 31574 | 26 | ||||
Duan (2019) [ |
2015-2017 | Clinical diagnosis | The American Diabetes Association (ADA) 2009 | Henan | Community-based | 40.2 | NA | NA | NA | 2710 | 26 | ||
Liu (2010) [ |
2007 | Clinical diagnosis | Clinical diagnosis | Multicenter (Shanghai, Chengdu, Beijing and Guangzhou) | Hospital-based | NA | 41.8 | 8.7 | NA | NA | 1524 | 26 |
Abbreviations: NA: not available; KDIGO: Kidney Disease Improving Global Outcomes; ADA: American Dental Association; KDOQI: Kidney Disease Outcomes Quality Initiative; CTM: Chinese Traditional Chinese Medicine Association.
A total of 30 studies, including 79,364 adults with T2DM, were evaluated. Substantial heterogeneity across the included studies was observed (
Forest plot displaying the pooled prevalence of DN in patients with type 2 diabetes in both population sources.
Table
Prevalence of DN by study and design characteristics.
Subgroups | No. of studies | Prevalence estimate (%) and 95% CI | Heterogeneity |
|
---|---|---|---|---|
Time | 0.73 | |||
≤2000 | 3 | 18.7 (9.6-33.3) | 98.6 | |
2001~2010 | 9 | 24.2 (19.0-30.4) | 96.5 | |
>2010 | 11 | 24.0 (19.4-29.3) | 98.7 | |
Diagnostic criteria for DN | <0.01 | |||
ADA criteria 1997 | 1 | 23.5 (19.7-27.9) | — | |
ADA criteria 2007 | 1 | 15.4 (14.0-17.0) | — | |
Clinical diagnosis | 18 | 21.8 (17.2-27.2) | 99.0 | |
CTM criteria 2010 | 1 | 18.0 (14.9-21.6) | — | |
KDIGO 2012 | 1 | 29.7 (29.2-30.2) | — | |
KDOQI 2007 | 1 | 18.5 (16.6-20.6) | — | |
KDOQI 2012 | 1 | 27.1 (25.6-28.7) | — | |
KDOQI 2014 | 1 | 39.4 (35.5-43.6) | — | |
Mogensen criteria | 2 | 9.5 (8.4-10.7) | 3.4 | |
Renal biopsy | 3 | 29.6 (7.9-67.3) | 96.7 | |
Diagnostic criteria for DM | <0.01 | |||
1985 WHO/1999 China diagnostic standards | 1 | 9.0 (7.7-10.5) | — | |
ADA criteria 2005 | 1 | 18.5 (16.6-20.6) | — | |
ADA criteria 2007 | 1 | 8.2 (5.4-12.4) | — | |
ADA criteria 2009 | 2 | 29.3 (19.1-42.2) | 97.5 | |
Clinical diagnosis | 8 | 24.7 (15.5-36.9) | 99.0 | |
History of DM | 6 | 35.3 (30.7-40.2) | 92.5 | |
WHO criteria 1985 | 2 | 13.9 (6.9-26.1) | 97.5 | |
WHO criteria 1999 | 8 | 16.9 (13.4-21.2) | 97.8 | |
WHO criteria 2004 | 1 | 18.0 (14.9-21.6) | — | |
Region | <0.01 | |||
Central region | 3 | 15.6 (4.9-39.8) | 99.6 | |
East region | 19 | 22.3 (18.6-26.5) | 97.4 | |
Northeast region | 3 | 20.7 (15.2-27.6) | 96.5 | |
West region | 2 | 41.3 (37.1-45.6) | 39.0 | |
Population source | 0.52 | |||
Community-based | 5 | 18.5 (10.0-31.5) | 99.0 | |
Hospital-based | 25 | 22.4 (18.8-26.5) | 99.1 | |
Age | 0.15 | |||
<60 | 12 | 24.8 (20.2-30.1) | 97.9 | |
≥60 | 9 | 19.5 (14.9-25.1) | 98.8 | |
BMI | 0.20 | |||
<25 | 4 | 14.4 (7.3-26.4) | 97.7 | |
≥25 | 5 | 23.8 (15.4-34.8) | 99.5 | |
Sex | <0.01 | |||
Male-dominated | 16 | 27.7 (24.1-31.7) | 97.3 | |
Female-dominated | 10 | 17.6 (12.6-24.0) | 99.2 | |
Urban and rural | 0.12 | |||
Rural | 2 | 26.2 (13.0-45.7) | 99.2 | |
Urban | 26 | 20.5 (17.1-24.3) | 99.1 | |
Urban and rural | 2 | 37.0 (21.2-56.2) | 96.2 | |
DM duration | 0.27 | |||
<8 | 7 | 26.0 (17.7-36.4) | 99.6 | |
8~9 | 5 | 17.4 (11.2-26.1) | 98.9 | |
10~ | 2 | 29.0 (14.1-50.4) | 98.8 | |
Sample size | 0.25 | |||
<1000 | 17 | 24.5 (18.8-31.3) | 97.6 | |
1000~3000 | 9 | 17.3 (12.4-23.6) | 98.9 | |
3000~ | 4 | 21.8 (13.7-33.0) | 99.8 | |
Quality | 0.47 | |||
20 | 5 | 26.3 (14.0-43.8) | 98.9 | |
≥20 | 25 | 20.9 (17.5-24.7) | 99.1 | |
Systolic blood pressure | 0.71 | |||
≥140 | 2 | 28.7 (7.6-66.2) | 97.6 | |
<140 | 4 | 22.5 (13.6-35.1) | 99.8 | |
Diastolic blood pressure | 0.61 | |||
≥80 | 3 | 17.0 (5.9-40.0) | 97.6 | |
<80 | 4 | 22.5 (13.6-35.1) | 99.8 |
To further understand regional differences, we performed stratified analyses by province/municipality. These analyses showed that the highest prevalence of DN was in the four provinces of Sichuan (43.8%), Gansu (39.4%), and Zhejiang and Henan (35.5%) provinces. Jiangsu (10.8%), Hubei (10.2%), and Hunan (9.0%) provinces had a low DN prevalence among patients with T2DM in China. The patterns of DN prevalence across the country are shown in Figure
Regional distribution of pooled prevalence of DN among patients with type 2 diabetes.
The mean (range) quality assessment score was 20 (12–26). Twenty-five studies had equal or higher than the mean strobe quality score (20 points) of all included studies, while 5 had lower than 20 points (Table
To the best of our knowledge, the present study is the first meta-analysis to estimate the pooled prevalence of DN in people with T2DM in China, which included 30 studies with 79,364 patients with T2DM. The pooled prevalence of DN showed that nearly one-fifth of patients with diabetes might have nephropathy complications. The detailed estimates in this study showed that diabetes complicated with nephropathy is a serious public health challenge for the health care system and may result in a large social and economic burden in China. Our findings could help in relevant policy-making and planning and allocation of health care resources.
The pooled DN prevalence in our study was in agreement with a German study (20–30%) [
Subgroup analyses were performed to evaluate the impact of different stratifications on the prevalence of pooled DN. We found that DN prevalence varied significantly according to different diagnostic criteria for DM and DN. In fact, over the past 40 years, the diagnosis criteria of DM and DN have been changed several times, and different diagnostic criteria might influence the diagnosis and surveillance of DN [
We found that the pooled prevalence of DN in the west region was the highest and that further stratified analyses by province/municipality showed that Sichuan and Gansu were the provinces with the highest prevalence of DN. Similarly, a study in the United States showed that there was geographic variation in adjusted incident rates of end-stage renal disease [
We also found that the pooled prevalence of DN was higher in the male-dominated studies than in the female-dominated studies, which echoed by the study of de Hauteclocque et al. [
Our study had several limitations. First, most studies included in our study were hospital-based, which might have led to an overestimation of DN prevalence among the T2DM population because of referral bias. Thus, dichotomized outcomes according to population source (hospital-based and community-based) were both provided, and this should be considered in interpreting our results. Second, potential heterogeneous factors, such as the different diagnostic criteria for DM and DN, and variation of study sample size, might add heterogeneity of pooled prevalence estimation. To evaluate the influence, subgroup analyses and leave-one-out sensitivity analysis were both used to quantify the potential impact.
In conclusion, our results indicate that the prevalence of DN in China is high and shows geographic and gender variation. National strategies aimed at primary and secondary prevention, as well as a geographically targeted screening program for DN among participants with T2DM, are urgently needed to reduce the increasing burden of DN in China.
The authors declare that they have no conflicts of interest.
XXZ and JK designed the study. KY and XXZ worked on data collection. XXZ analyzed the data, interpreted the results, and wrote the paper. All authors supplied comments and revised the manuscript and approved the manuscript before submission.
The study was supported by the China Postdoctoral Science Foundation (2017M621175).
Supplement Table S1: methodological quality assessment results for the included studies. Supplement Figure S1: funnel plot of estimation of DN prevalence.