Nomogram for Prediction of Diabetic Retinopathy Among Type 2 Diabetes Population in Xinjiang, China

Figures & data

Figure 1 Flow diagram of the data screening.

Notes: diagnosis unrecognized: Comprehensive eye examinations without mydriasis; incomplete clinical data: Delete samples missing more than 2 variables; DR did not meet the requirements: retinopathy caused by hypertension; abnormal data: data that cannot be included in the evaluation due to severe cataracts and other reasons; 76 patients in incomplete clinical data and DR did not meet the requirements were repeated.

Table 1 Clinical Characteristics of the Study Population (N=13,980)

Variables		Without DR	With DR	P value
		n=8071	n=5909
DPN (%)
	No	6768 (83.9%)	1552 (26.3%)	<0.001^a
	Yes	1303 (16.1%)	4357 (73.7%)
Gender (%)
	Male	5366 (66.5%)	3321 (56.2%)	<0.001^a
	Female	2705 (33.5%)	2588 (43.8%)
Hypertension (%)
	No	4253 (52.7%)	2567 (43.4%)	<0.001^a
	Yes	3818 (47.3%)	3342 (56.6%)
Age (%)
	≤25	77 (1.0%)	8 (0.1%)	<0.001^a
	25–40	951 (11.8%)	180 (3.0%)
	40–55	3683 (45.6%)	1902 (32.2%)
	55–70	2409 (29.8%)	2650 (44.8%)
	>70	951 (11.8%)	1169 (19.8%)
WBC		7.24±2.95	6.82±2.46	<0.001^b
NE		4.42±2.52	4.04±1.78	<0.001^b
EOS		0.16±0.24	0.17±0.15	0.862^b
LY		2.20±0.81	2.17±0.85	0.016^b
Tc		4.44±1.21	4.43±1.22	0.743^b
HDL		1.01±0.32	1.10±0.33	<0.001^b
LDL		2.76±0.90	2.79±0.89	0.036^b
AST		23.03±20.30	20.29±13.27	<0.001^b
ALT		29.61±29.26	24.02±22.29	<0.001^b
Duration of T2DM (%)
	≤5	5097 (63.2%)	1359 (23.0%)	<0.001^a
	5–15	2369 (29.4%)	2929 (49.6%)
	15–25	548 (6.8%)	1440 (24.4%)
	>25	57 (0.7%)	181 (3.1%)
GLC		9.47±5.12	10.21±5.33	<0.001^b
HbA1C		8.36±2.06	9.09±2.07	<0.001^b
GSP		2.79±0.80	2.97±0.80	<0.001^b
ApoA1		1.17±0.26	1.22±0.25	<0.001^b
ApoB		0.94±0.29	0.93±0.29	0.12^b
TG		2.49±2.51	2.18±2.08	<0.001^b
Urea		6.07±8.25	6.24±11.84	0.299^b

Note: ^aRefers to chi square test, ^brefers to t-test.

Abbreviations: DPN, diabetic peripheral neuropathy; WBC, white blood cell; NE, neutrophilic granulocyte; EOS, eosinophil; LY, lymphocyte; Tc, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GLC, glucose; HbA1C, Hemoglobin A1c; GSP, glycosylated serum protein; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; TG, triglyceride.

Table 2 Baseline Characteristics Were Analyzed by Multivariate Logistic Regression in the Training Group. (N= 10,485)

	OR (95% CI)	P value
DPN
No	1
Yes	13.653 (12.261–15.220)	<0.001
Age	1.039 (1.034–1.044)	<0.001
WBC	1.043 (1.001–1.091)	0.0574
NE	0.846 (0.801–0.891)	<0.001
HDL	1.856 (1.579–2.183)	<0.001
HbA1C	1.291 (1.252–1.331)	<0.001
Duration of T2DM	1.090 (1.081–1.100)	<0.001
GSP	1.120 (1.038–1.209)	0.004

Abbreviations: DPN, diabetic peripheral neuropathy; WBC, white blood cell count; NE, neutrophilic granulocyte; HDL, high-density lipoprotein; HbA1C, Hemoglobin A1c; GSP, Glycosylated Serum Protein.

Figure 2 Demographic and clinical feature selection using the LASSO binary logistic regression model (figure was created by R software, “glmnet” package, version 2.0–18, https://CRAN.R-project.org/package=glmnet).

Notes: (A) Optimal candidate (lambda) selection in the LASSO model used 5-fold cross validation via minimum criteria. The area under the receiver operation characteristic curve was plotted versus log (lambda). Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the 1 SE of the minimum criteria; (B) LASSO coefficient pro-files of the 21 candidates. A coefficient profile plot was produced against the log (lambda) sequence. Vertical line was drawn at the value selected using 5-fold cross validation, where optimal lambda resulted in 8 candidates with nonzero coefficients.

Figure 3 Nomogram to predict the risk of DR.

Notes: To use the nomogram, an individual participants value is located on each variable axis, and a line is drawn upward to determine the number of points received for each variable value. The sum of these numbers is located on the total points axis to determine the risk of DR.

Figure 4 According to Nomogram’s estimate, the probability of DR risk in patient no.1020 (A) and no.2089 (B) was 0.12 and 0.816, respectively, P < 0.001.

Notes: Figure a and b are the line segment dynamic nomogram, which is different from the line segment static nomogram in the paper. After optimization corrected, each variable starts with 0 (nomogram). In the line segment dynamic nomogram, it is uncorrected and reflects the score corresponding to each variable in the actual situation. Therefore, the score corresponding to each variable starts from the specific value rather than 0. Although the scores of the two are different, the prediction probability is the same.

Figure 5 The ROC curves of the nomogram for DR risk (left, development group, right, validation group. including ROC curves of single risk factor model).

Notes: In the development group, the AUCs were 0.882 (95% CI: 0.875–0.888); In the validation group, the AUCs were 0.870 (95% CI: 0.856–0.881), respectively. ROC, receiver operating characteristics curve, AUC, area under curve.

Figure 6 Calibration curves for the validation and development group models (left, development group, right, validation group).

Notes: The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the nomogram, of which a closer fit to the diagonal dotted line represents a better prediction.

Figure 7 The decision curve analysis of the nomogram for DR risk (left, development group, right, validation group).

Notes: The red line represents the net benefit when no participant was considered to exhibit DR, while the blue line represents the net benefit when all participants were considered to suffer from DR. The area among the model curve, “treat none line” (red line) and “treat all line” (blue line), represents the clinical usefulness of the model. The farther the model curve is to the blue and red lines, the better clinical value the nomogram holds.