| Literature DB >> 36117980 |
Li Zhang1, Shuai Xue2, Meiyan Wu1, Dan Dong1.
Abstract
Aims: Diabetic nephropathy (DN) is one of the main causes of chronic kidney disease (CKD), which increases the risk of cardiovascular diseases and progresses to end-stage renal failure. Thus, early diagnostic markers for diabetic patients are urgently needed to improve the prognosis of DN and predict DN progression. Materials and methods: PubMed, MEDLINE, EMBASE, and Scopus were searched for publications until February 24, 2021. Review Manager 5.4 software was used for meta-analysis. We performed the heterogeneity test using the I2 statistic: P < 0.1 and I2> 50% meant statistical significance.Entities:
Keywords: biomarkers; chronic kidney disease (CKD); diabetic kidney disease; meta-analysis; urinary liver-type fatty acid binding protein
Year: 2022 PMID: 36117980 PMCID: PMC9479543 DOI: 10.3389/fmed.2022.914587
Source DB: PubMed Journal: Front Med (Lausanne) ISSN: 2296-858X
FIGURE 1Flow chart of screening process.
Characteristics of the included studies.
| Author | Year | Country/Region | Study Design | DM type | Sample size | Sex (Male/Female) | Age (year, mean ± SD or median (range)) | Method | NOS |
| Abd El Dayem ( | 2015 | Egypt | cross-sectional | 1 | 92 | DM: 31/31 | 16.32 ± 1.52 | ELISA | 6 |
| C: 15/15 | 16.3 ± 2.63 | ||||||||
| Nielsen ( | 2010 | Denmark | cross-sectional | 1 | 204 | 118/86 | 38 ± 12.6 | ELISA | 8 |
| Panduru ( | 2013 | Australia | cross-sectional | 1 | 2454 | DM: 1126/1120 | NA | ELISA | 8 |
| C: 106/102 | 35.9 ± 11.3 | ||||||||
| Suh ( | 2016 | South Korea | cross-sectional | 1 | 61 | DM: 12/17 | NA | ELISA | 7 |
| C: 13/19 | 11.91 ± 3.61 | ||||||||
| Chou ( | 2013 | Taiwan/China | longitudinal | 2 | 140 | 72/68 | 56.6 ± 9.8 | ELISA | 6 |
| Thi ( | 2020 | Vietnam | cross-sectional | 2 | 136 | 60/76 | NA | ELISA | 7 |
| Eynatten ( | 2010 | Germany | cross-sectional | 2 | 170 | 125/45 | DM:60.7 ± 7.4 | ELISA | 8 |
| C:51.9 ± 9.5 | |||||||||
| Fufaa ( | 2015 | the Gila River Indian Community | longitudinal | 2 | 260 | 82/178 | 42.5(18.7-65.1) | ELISA | 8 |
| Gohda ( | 2018 | Japan | cross-sectional | 2 | 314 | 166/148 | 64 ± 13 | ELISA | 7 |
| Ito ( | 2017 | Japan | cross-sectional | 2 | 788 | 457/331 | 66 ± 12 | ELISA | 6 |
| Kamijo ( | 2011 | Japan | cross-sectional | 2 | 552 | 88/52 | NA | ELISA | 7 |
| Suzuki ( | 2005 | Japan | cross-sectional | 2 | 356 | 229/127 | 63 ± 11 | ELISA | 7 |
| Viswanathan ( | 2015 | India | cross-sectional | 2 | 78 | 45/33 | NA | ELISA | 7 |
DM, diabetes mellitus; C, control; NOS, Newcastle-Ottawa Scale; NA, Not Available; ELISA, enzyme linked immunosorbent assay.
FIGURE 2(A) Forest plot of u-LFABP level comparison in normal control group and diabetic patients with normal albuminuria group; (B) Forest plot of u-LFABP level comparison in diabetic patients with micro albuminuria group and normal albuminuria group; (C) Forest plot of u-LFABP level comparison in diabetic patients with macro albuminuria group and micro albuminuria group.
FIGURE 3(A) Meta-analysis Forest plots of u-LFABP concentrations comparison in progressive DM group and non-progressive DM group; (B) Meta-analysis Forest plots of u-LFABP concentrations comparison in DM with CKD and DM without CKD.
FIGURE 4Forest plots of correlation analysis between u-LFABP values and different clinical index of ACR (A), eGFR (B), SBP (C), HbA1c (D), FPG (E).
FIGURE 5Subgroup analysis of different albuminuria groups in DM according to diabetes types (A) Normal albuminuria group vs. Normal control group; (B) Miro albuminuria group vs. Normal albuminuria group; (C) Macro albuminuria group vs. Micro albuminuria group.
FIGURE 6Subgroup analysis for DM with progressive group.