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基于糖尿病视网膜病变的诊断模型对糖尿病肾病有较好诊断效能

Validation and comparison of diabetic retinopathy-based diagnostic models for diabetic nephropathy

李莹 , ^1, * 王倩 , ² 陈小鸟 , ¹ 席悦 , ² 杨建 , ² 刘晓敏 , ² 王远大 , ² 张利 , ² 蔡广研 , ² 陈香美 , ² and 董哲毅 ^2, ^*

李莹

中国人民解放军总医院第三医学中心眼科医学部，北京 100039, Senior Department of Ophthalmology, Third Medical Center of Chinese PLA General Hospital, Beijing 100039, China

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王倩

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陈小鸟

中国人民解放军总医院第三医学中心眼科医学部，北京 100039, Senior Department of Ophthalmology, Third Medical Center of Chinese PLA General Hospital, Beijing 100039, China

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席悦

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杨建

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刘晓敏

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王远大

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张利

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蔡广研

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陈香美

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董哲毅

中国人民解放军总医院第一医学中心肾脏病医学部//解放军肾脏病研究所//肾脏疾病全国重点实验室//国家慢性肾病临床医学研究中心//肾脏疾病研究北京市重点实验室，北京 100853, Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China 中国人民解放军总医院第三医学中心眼科医学部，北京 100039, Senior Department of Ophthalmology, Third Medical Center of Chinese PLA General Hospital, Beijing 100039, China 中国人民解放军总医院第一医学中心肾脏病医学部//解放军肾脏病研究所//肾脏疾病全国重点实验室//国家慢性肾病临床医学研究中心//肾脏疾病研究北京市重点实验室，北京 100853, Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China

Gh：糖化血红蛋白(≥7%赋值为1， < 7%赋值为0)；Hb：血红蛋白(g/L)；Hu：合并血尿(尿RBC>10/HP赋值为1，≤10/HP赋值为0)，余同NDT模型。

以上两模型，PDN为诊断DN的概率，PDN≥0.5诊断为DN，PDN < 0.5诊断为NDRD。

1.5. 统计学分析

采用SPSS软件(Version 17.0版本)进行统计分析。用Kolmogorov-Smirnov检验对连续变量进行正态性检验；对于正态分布连续变量的统计量用均数±标准差表示，两组间比较采用 t 检验；3组间总体分布的比较采用单因素方差分析，若总体分布具有统计学差异，进一步采用Scheffe法进行两两比较。对于偏态分布计量资料的统计量用中位数(四分位间距)表示，组间比较采用Wilcoxon秩和检验；分类变量的统计量用构成比(%) 表示，对于两组间构成比的比较采用χ ² 检验；对于3组间构成比的总体分布比较采用R×C无序列联表多个样本构成的χ ² 检验，若总体分布具有统计学差异，进一步进行两两χ ² 检验，此时 P <0.05表示差异具有统计学意义。采用MedCalc Version软件(11.2.1版本)绘制受试者ROC曲线，并比较ROC曲线下面积(AUC)。研究中所有检验均为双侧性检验，如果 P <0.05表示差异具有统计学意义(除外两两χ ² 检验)，如果 P <0.01则表示差异具有显著统计学意义。

2. 结果

2.1. DN和NDRD患者不同时期临床特征的比较分析

早、中、晚3个时期DN组高血压及DR的发生率，HbA1c、肌酐及尿素氮、24 h尿蛋白定量均高于NDRD组，血红蛋白、甘油三酯均低于NDRD组( P <0.01)，高尿酸的发生率、血尿酸、胆固醇在两组间差异均无统计学意义( 表 1 )。

表 1

早中晚期DN组与NDRD组患者临床特征的比较

Comparison of clinical characteristics of DN and NDRD cases collected in the early, middle, and late stages

Variables	DN Group			NDRD Group
Variables	Early stage ( n =60)	Middle stage ( n =93)	Late stage ( n =106)	Early stage ( n =50)	Middle stage ( n =107)	Late stage ( n =149)
* Middle stage versus Early stage P <0.05;** Middle stage versus Early stage P <0.01. ^# Late stage versus Middle stage P <0.05; ^## Late stage versus Early stage P <0.01; ^▲ Late stage versus Middle stage P <0.05; ^▲▲ Late stage versus Middle stage P <0.01.
Gender (male, %)	37 (61.7%)	59 (63.4%)	79 (69%)	40 (80%)	78 (72.8%)	97 (65.1%)
Age (year)	-	51.6±9.6	50.6±10.0	-	47.7±11.4	50.2±10.7
BMI (kg/m ² )	24.1±2.9	26.0±3.0**	26.8±3.8 ^##	25.8±2.7	27.2±3.8*	27.2±4.2 ^#
History of diabetes (month)	87.6±54.3	139.7±75.3**	156 (63-192) ^##	26.3±18.8	52.3±29.6**	36 (8-72) ^##
Hypertension (%)	46 (76.6%)	81 (87.1%)	95 (89.6%)	25(50%)	62 (57.9%)	102 (68.5%)
Systolic pressure (mmHg)	149.2±22.3	152.1±19.0	153.0±24.4	133.7±17.9	136.3±20.5	135.9±17.4
DR (%)	46 (76.6%)	73 (78.5%)	87 (82.1%)	5 (10.0%)	16 (15.0%)	13 (8.7%)
Hemoglobin (g/L)	122.3±27.0	111.7±21.1	112.1±19.3	144.1±19.9	137.3±25.0	133.7±19.5 ^##
Serum albumin (g/L)	31.2±8.2	32.3±6.0	32.8±6.1 ^#	35.6±8.8	35.2±9.0	32.3±9.5 ^#▲
HbA1c (%)	8.4±2.0	7.3±1.6**	7.1±1.8 ^##	7.0±1.5	6.8±1.0	6.7±1.3
Serum creatinine (μmol/L)	128.8±69.5	133.3±82.1	147.2±81.4	97.3±50.0	104.6±60.4	94.8±49.0
eGFR (mL/min/1.73m ² )	-	60.0±28.5	64.0±32.9	-	74.3±28.2	78.5±33.3
Uric acid (μmol/L)	352.2±92.9	362.1±91.1	368.6±75.5	371.8±95.5	356.3±100.4	348.5±99.2
Triglyceride (mmol/L)	1.86±1.18	1.6 (1.2-2.4)	1.7 (1.2-2.4)	3.19±2.65	2.3 (1.6-3.0)	2.0 (1.4-3.0)
Total cholesterol (mmol/L)	6.09±2.04	5.1±1.7**	5.4±2.1 ^##	6.30±2.46	5.5±1.9	6.0±2.5
Hematuresis (%)	10 (16.7%)	30 (32.3%)	40 (37.7%) ^##	34 (68.0%)	46 (43.0%)**	84 (56.4%)
Urine protein quantitation (g/d)	4.10±2.96	2.8 (1.9-4.6)	4.3 (2.1-6.4) ^▲	3.06±3.08	1.8 (0.7-3.9)	2.9 (1.3-5.8) ^▲▲

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2.2. 不同时期NDRD病理类型分布的比较

早、中、晚3个时期，NDRD的比例分别为45.5%、53.5%和58.4%，最主要的两种病理类型均为IgA肾病和膜性肾病。在NDRD的病理类型构成中，早期和中期IgA肾病占第1位，分别占34%和32.7%，晚期IgA肾病的比例虽然升至36.9%，但在NDRD构成比排名中下降至第2位，膜性肾病升至首位，占39%( 图 1 )。

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图 1

早、中、晚期NDRD组病理类型分布

Distribution of pathological types of NDRD cases collected in early, middle and late stages.

2.3. 对JDB模型和NDT模型的评价和比较

两模型中，JDB模型诊断的真实性较好，Youden指数为0.8，准确度为89.9%，能较好的对DN和NDRD患者进行鉴别；虽然NDT模型诊断的符合率差于JDB模型，准确度为80.78%，但其特异度高达98.66%，可高效的鉴别出NDRD患者( 表 2 )

表 2

JDB模型和NDT模型诊断试验评价指标

Diagnostic test evaluation indexes of JDB model and NDT model

Evaluation indexes	JDB model	NDT model
Sensitivity (%)	89.62	55.66
Specificity (%)	89.93	98.66
Youden index	0.8	0.54
Positive likelihood ratio	8.9	41.47
Negative likelihood ratio	0.12	0.45
Positive predictive value (%)	86.36	96.72
Negative predictive value (%)	92.41	75.77
Accuracy (%)	89.8	80.78

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用AUC评价两模型诊断效能的优劣，发现JDB模型AUC为0.946(95% C I 0.911~0.971)，NDT模型AUC为0.925(95% CI 0.886~0.954)，但两者AUC的差异无统计学意义( P =0.198)。

2.4. 对JDB模型和NDT模型误判病例的评价和比较

JDB模型误判病例数为26例，其中将NDRD误判为DN 15例，将DN误判为NDRD 11例。病理诊断为NDRD的149例患者中，被JDB模型误判为DN者15例，与被正确判断的134例NDRD患者比较发现，误判的患者T2DM病史较长，血尿的发生率低，DR的发生率高( P <0.05)。而收缩压、糖化血红蛋白、血红蛋白在两组患者中无差异。病理诊断为DN的106例患者中，被JDB模型误判为NDRD者11例，与被正确判断的95例DN比较发现，误判的患者T2DM病史较短，血尿的发生率高，DR的发生率低，且血红蛋白较高( P <0.05)。而收缩压、HbA1c在两组患者中无差异。

NDT模型误判病例数为49例，其中将NDRD误判为DN 2例，将DN误判为NDRD 47例。病理诊断为DN的106例患者中，被NDT模型误判为NDRD者47例，与被正确判断的59例DN比较发现，误判的患者T2DM病史较短，血尿的发生率高( P <0.05)。而收缩压、HbA1c、DR在两组患者中无差异。病理诊断为NDRD的149例患者中，被NDT模型误判为DN者仅2例，无法与被正确判断的147例NDRD进行统计学比较。

3. 讨论

利用数学方法建立概率诊断模型，为疾病提供可量化的无创诊断策略，是实施临床诊断、提高诊断准确率和可重复性的有力手段。Logistic回归作为传统模型在很多研究中效能高，在预测慢性病风险方面与机器学习模型具有相同的性能 ^{[

12

]} ，诊断模型的变量权重明确、直观、可解释性好，模型可以直接使用，对于临床医生学习成本低。诊断模型已在动脉粥样硬化等心血管疾病 ^{[

13

]} 、慢性肾病 ^{[

12

]} 、急性肾损伤 ^{[

14

]} 等多类疾病的诊断和预测中得到应用。

1型糖尿病肾病(T1DN)临床特征典型，若DM病史>10年，合并DR，病理证实DN诊断符合率达95%以上，不必须行肾活检 ^{[

15

]} 。2型糖尿病肾病(T2DN)的拟诊证据多数源自T1DN，并因患者基数庞大，肾活检率不足25% ^{[

6

]} 。而T2DN患者临床表现复杂，DN和NDRD的鉴别困难 ^{[

16

]} ，准确率低。本团队的系列研究首次基于肾活检“金标准”建立的logistic模型鉴别DN和NDRD，能够合理分配各临床参数的权重，使临床经验和临床证据的结合可量化、科学性和实用价值更强 ^{[

9

,

10

]} 。

在Logistic回归数学模型的多种临床参数中，DR是DN诊断的最主要依据之一。中国人群中DN和DR相关性的研究发现，经肾活检确诊为T2DN的患者中，48.8%伴有DR ^{[

17

]} 。患有DR的患者DN的风险增加31% ^{[

18

]} 。DR与DN具有共同的危险因素，并与DN的的发病机制相似，涉及氧化应激 ^{[

19

]} 、糖基化终末产物的大量堆积 ^{[

20

]} 、遗传因素 ^{[

21

]} 等。DR的严重程度是2型糖尿病患者进展为CKD的危险因素 ^{[

22

]} ，视网膜血管口径变化可以预测DN的风险 ^{[

23

]} ，DR血管异常是肾功能受损的预测因子，而微量白蛋白尿是DR进展的准确生物标志物 ^{[

24

]} ，同时DN是DR发生和进展的独立危险因素 ^{[

25

]} 。在本研究不同时期所建立的Logistic回归数学模型中DR都具有较高的权重。虽然DR对DN有重要的辅助诊断价值，但诊断标准的主观性强，将来需联合更多的客观指标进行综合判断。

HbA1c是评价糖尿病血糖控制的有效指标，也是DN发生风险预测的重要参数 ^{[

26

,

27

]} 。虽然各个时期DN组HbA1c水平均高于NDRD组，但随时间变迁两组的HbA1c水平均出现了下降。2022年ADA指南 ^{[

28

,

29

]} 建议如果患者无明显的低血糖或其他治疗副作用，将HbA1c控制在6.5%以下更合理。我国40岁以上人群研究数据显示，HbA1c≥5.5% 与尿白蛋白/肌酐比值呈正相关且独立相关 ^{[

30

]} 。这都促使糖尿病患者血糖控制更加严格。除此之外，我们还发现DN组HbA1c水平随时间变迁下降更显著。在肾病患者中容易受肾性贫血、红细胞周期缩短等干扰因素的影响而造成结果假性降低 ^{[

31

]} 。由于DN组肾功能更差、贫血更严重，DN患者的HbA1c可能低于实际水平。

合并血尿是拟诊NDRD的重要依据之一，中晚期患者血尿的总体发生比例较早期增加，这与肾活检指征的改变有关。随着就诊患者量的激增以及对临床证据和指南 ^{[

32

-

34

]} 掌握的不断完善，临床医生越来越倾向于仅对拟诊NDRD的患者行肾活检病理检查 ^{[

5

]} 。其中，3个时期NDRD患者比例的逐渐增加(分别为45.5%、53.5%、58.4%)也印证了肾活检指征的变化

早期的NDT模型 ^{[

9

]} 回代验证灵敏度为90%，特异度为92%，AUC达0.9677。中期数据对模型进行验证，发现诊断效能有所下降，考虑与模型中临床参数随时间发生变迁有关，因此对NDT模型进行了“与时俱进”的优化，新建立JDB鉴别诊断模型 ^{[

10

]} 。前瞻性验证证实新模型诊断的灵敏度为84.2%，特异度为94.4%，AUC达0.971。晚期数据验证JDB模型的Youden指数为0.8，准确度为89.9%，说明它能够较好的区分DN和NDRD患者，总体鉴别能力较好。NDT模型特异度高达98.66%，说明它能够较好的鉴别出NDRD患者，但由于灵敏度仅为55.66%，因此对DN患者的识别能力较差。JDB模型的AUC比NDT模型高0.0212，但总体诊断效能的差异无统计学意义( P =0.1982)。

通过对误判病例与正确判断病例的各模型参数进行比较，明确被误判的患者特点，在已经模型判断分组的患者中，挑出具备这些特点的患者，进行综合分析和临床鉴别诊断，是减少误判率，更好利用模型的方法。误判分析结果显示，在两模型的各临床参数中，是否合并DR、病史长短、有无血尿是影响模型判断准确度的重要因素。

IgA肾病和膜性肾病一直以来都是我中心NDRD的主要病理构成 ^{[

35

]} ，但早期和中期都以IgA肾病为主(分别为34%和32%)，晚期IgA肾病的构成比与前期基本持平(37%)，但膜性肾病的比例显著增加，由前两个时期的22%和19%上升为39%，首次成为NDRD的最主要病理类型。我们中心前期大样本的研究中膜性肾病患者占NDRD患者的33.55% ^{[

36

]} ，在伊朗和土耳其两项单中心研究中膜性肾病患者也是NDRD的首位病理类型构成，所占比例分别为34% ^{[

37

]} 、62.5% ^{[

38

]} 。

总之，通过基于DR的DN诊断模型效能的分析，我们认为NDT诊断NDRD的符合率较高，是肾活检指征的有益补充。而当肾活检开展受限的情况下，可使用JDB模型进行诊断推断。期待通过多中心大样本的数据积累和眼底照片的特征分析，对DN和NDRD模型进行优化和效能提升，为疾病的无创鉴别诊断提供更确信可靠的前沿方法。

Biography

•

李莹，硕士，主治医师，E-mail: nc.moc.anis@1310_gniyil

Funding Statement

国家自然科学基金(62250001，81700629，32000530)；北京市自然科学基金(L222133)；北京市科技计划课题(Z221100007422121)；国家博士后创新人才支持计划(BX20190382)

Funding Statement

Supported by Natural Science Foundation of China (62250001, 81700629, 32000530)

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Articles from Journal of Southern Medical University are provided here courtesy of Editorial Department of Journal of Southern Medical University

基于糖尿病视网膜病变的诊断模型对糖尿病肾病有较好诊断效能

Validation and comparison of diabetic retinopathy-based diagnostic models for diabetic nephropathy

李 莹

王 倩

陈 小鸟

席 悦

杨 建

刘 晓敏

王 远大

张 利

蔡 广研

陈 香美

董 哲毅

1.5. 统计学分析

2. 结果

2.1. DN和NDRD患者不同时期临床特征的比较分析

表 1

2.2. 不同时期NDRD病理类型分布的比较

2.3. 对JDB模型和NDT模型的评价和比较

表 2

2.4. 对JDB模型和NDT模型误判病例的评价和比较

3. 讨论

Biography

Funding Statement

Funding Statement

References

李莹

王倩

陈小鸟

席悦

杨建

刘晓敏

王远大

张利

蔡广研

陈香美

董哲毅