Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese

Table 1 Demographic, biochemistry, and lifestyle information of the participants

	Low eGFR	High eGFR
Number	180	1056
Age (yr)	72.1 ± 5.9	69.5 ± 4.6c
Sleep time (h)	5.89 ± 1.10	6.1 ± 1.15a
Drinking duration	4.76 ± 4.37	5.25 ± 5.74
Sport hours	205.6 ± 36.2	204.4 ± 36.3
Body mass index (kg/m2)	23.9 ± 3.4	23.7 ± 3.2
White blood cell count (103/μL)	5.94 ± 1.75	5.58 ± 1.40b
Hemoglobin (g/dL)	13.8 ± 1.5	14.0 ± 1.3
Fasting plasma glucose (mg/dL)	108.2 ± 19.3	108.2 ± 21.5
Alkaline phosphatase (IU/L)	67.8 ± 19.2	66.6 ± 22.2
Serum glutamic oxaloacetic transaminase (IU/L)	27.3 ± 11.3	26.0 ± 12.8
Serum glutamic pyruvic transaminase (IU/L)	25.4 ± 14.7	25.8 ± 19.2
γ-glutamyltransferase (IU/L)	28.5 ± 28.7	27.1 ± 35.5
Systolic blood pressure (mmHg)	131.2 ± 18.9	127.4 ± 18.1b
Diastolic blood pressure (mmHg)	75.6 ± 11.2	74.6 ± 10.6
Triglyceride (mg/dL)	121.4 ± 71.4	114.5 ± 67.3
High density lipoprotein cholesterol (mg/dL)	57.4 ± 14.3	59.6 ± 16.0
Uric acid (mg/dL)	6.53 ± 1.48	5.56 ± 1.3
High sensitivity C-reactive protein (mg/L)	2.52 ± 5.08	2.11 ± 4.35c
eGFR	74.8 ± 10.1	53.02 ± 6.8

^aP < 0.05.

^bP < 0.01.

^cP < 0.005.

eGFR: Estimated glomerular filtration rate.

Table 2 Variables contained in three models

	Model 1	Model 2	Model 3
Age	√	√	√
Body mass index	√	√	√
Systolic blood pressure	√	√	√
Diastolic blood pressure	√	√	√
Fasting plasma glucose	√	√	√
Serum glutamic oxaloacetic transaminase	√	√	√
Serum glutamic pyruvic transaminase	√	√	√
Uric acid	√	√	√
High density lipoprotein cholesterol	√	√	√
High sensitivity C-reactive protein	√	√	√
Triglyceride	√	√	√
Estimated glomerular filtration rate	√	√	√
Sleep time		√	√
Drinking duration		√	√
Sport hours		√	√
White blood cell count			√
Hemoglobin			√
Alkaline phosphatase			√
γ-glutamyltransferase			√
High sensitivity C-reactive protein			√

Table 3 Results of five different machine learning methods in three different models

Model	Methods	Accuracy	Sensitivity	Specificity	AUC	F1-score	BA
Model 1	MARS	0.689	0.717	0.519	0.633	0.799	0.618
	XGboost	0.715	0.753	0.482	0.602	0.820	0.617
	SGB	0.596	0.590	0.630	0.599	0.715	0.610
	LightGBM	0.731	0.771	0.482	0.615	0.831	0.626
	Catboost	0.549	0.518	0.741	0.623	0.664	0.629
Model 2	MARS	0.689	0.683	0.731	0.693	0.792	0.707
	XGboost	0.632	0.617	0.731	0.663	0.744	0.674
	SGB	0.762	0.802	0.500	0.666	0.854	0.651
	LightGBM	0.767	0.808	0.500	0.646	0.857	0.654
	Catboost	0.637	0.635	0.654	0.663	0.752	0.644
Model 3	MARS	0.767	0.801	0.622	0.760	0.848	0.711
	XGboost	0.762	0.763	0.757	0.786	0.838	0.760
	SGB	0.777	0.789	0.730	0.814	0.851	0.759
	LightGBM	0.741	0.750	0.703	0.776	0.824	0.726
	Catboost	0.819	0.897	0.487	0.744	0.889	0.692

AUC: Area under receiver operating characteristic curve; BA: Balanced accuracy; MARS: Multivariate adaptive regression splines; XGBoost: eXtreme Gradient Boosting; SGB: Stochastic gradient boosting; LightGBM: Light Gradient Boosting Machine; CATboost: Categorical Features + Gradient Boosting. Model 1: Demographic and biochemistry data; Model 2: Model 1 + lifestyle factors; Model 3: Model 2 + inflammation factors.

Table 4 Area under receiver operating characteristic curve derived from five different machine learning methods of the three different models

Model/AUC	Model 1	Model 2	Model 3
MARS	0.633	0.693	0.760
XGboost	0.602	0.663	0.786
SGB	0.599	0.666	0.814
LightGBM	0.615	0.646	0.776
Catboost	0.623	0.663	0.744
Mean	0.6144	0.6662	0.776

AUC: Area under receiver operating characteristic curve, MARS: Multivariate adaptive regression splines; XGBoost: eXtreme Gradient Boosting; SGB: Stochastic gradient boosting; LightGBM: Light Gradient Boosting Machine; CATboost: Categorical Features + Gradient Boosting. Model 1: Demographic and biochemistry data; Model 2: Model 1 + lifestyle factors; Model 3: Model 2 + inflammation factors.

Table 5 Rank importance of risk factors in model 1, from the most important to the least

Variable	MARS	XGboost	SGB	LightGBM	Catboost	AVG
Uric acid	1	1	1	1	1	1
Age	1	2	2	2	2	1.8
Body mass index	11	3	8	4	3	5.8
HDL-cholesterol	11	4	4	9	5	6.6
Systolic blood pressure	11	6	5	5	7	6.8
Serum glutamic pyruvic transaminase	4	9	7	10	4	6.8
Fasting plasma glucose	5	10	10	3	9	7.4
Diastolic blood pressure	11	5	6	8	8	7.6
Gender	3	8	11	7	10	7.8
Triglyceride	11	11	3	6	11	8.4
Serum glutamic oxaloacetic transaminase	11	7	9	11	6	8.8

AVG: Average of the rank of importance; MARS: Multivariate adaptive regression splines; XGBoost: eXtreme Gradient Boosting; HDL: High-density lipoprotein; SGB: Stochastic gradient boosting; LightGBM: Light Gradient Boosting Machine; CATboost: Categorical Features + Gradient Boosting. Model 1: Demographic and biochemistry data; Model 2: Model 1 + lifestyle factors; Model 3: Model 2 + inflammation factors.

Table 6 Rank importance of risk factors in model 2, from the most important to the least

Variable	MARS	XGboost	SGB	LightGBM	Catboos	AVG
Uric acid	1	1	1	1	1	1
Age	2	2	2	4	2	2.4
Body mass index	2	5	2	2	3	2.8
Triglyceride	7	4	5	1	5	4.4
Diastolic blood pressure	3	7	3	9	2	4.8
Sport hours	4	6	6	10	4	6
Systolic blood pressure	14	1	4	3	11	6.6
Serum glutamic oxaloacetic transaminase	6	8	8	5	9	7.2
HDL-cholesterol	14	3	7	8	6	7.6
Fasting plasma glucose	14	10	10	6	10	10
Drinking	5	12	12	14	8	10.2
Sleep time	14	9	9	14	7	10.6
Serum glutamic pyruvic transaminase	14	11	11	7	13	11.2
Gender	14	13	14	14	12	13.4
Smoking	14	14	14	14	14	14

AVG: Average of the rank of importance; MARS: Multivariate adaptive regression splines; XGBoost: eXtreme Gradient Boosting; HDL: High-density lipoprotein; SGB: Stochastic gradient boosting; LightGBM: Light Gradient Boosting Machine; CATboost: Categorical Features + Gradient Boosting. Model 2: Model 1 + lifestyle factors.

Table 7 Rank importance of risk factors in model 3, from the most important to the least

Variable	MARS	XGboost	SGB	LightGBM	Catboost	AVG
Uric acid	1	1	1	1	1	1
Age	1	2	2	2	4	2.2
Hemoglobin	4	4	6	3	3	4
Body mass index	7	3	3	7	2	4.4
Sport hours	3	8	4	5	13	6.6
Systolic blood pressure	5	6	20	4	5	8
Diastolic blood pressure	20	5	5	6	14	10
Alkaline phosphatase	20	7	11	12	10	12
γ-glutamyl transferase	20	13	8	9	12	12.4
Hs-C reactive protein	9	12	10	15	17	12.6
Fasting plasma glucose	20	9	20	10	7	13.2
Serum glutamic oxaloacetic transaminase	20	18	7	13	8	13.2
HDL-cholesterol	20	10	13	8	15	13.2
Drinking	20	14	12	16	6	13.6
White blood cell count	8	11	20	11	18	13.6
Sleep time	6	17	20	18	11	14.4
Serum glutamic pyruvic transaminase	20	16	14	14	9	14.6
Triglyceride	20	15	9	17	16	15.4
Gender	20	20	20	20	19	19.8
Smoking	20	20	20	20	20	20

AVG: Average of the rank of importance; MARS: Multivariate adaptive regression splines; XGBoost: eXtreme Gradient Boosting; HDL: High-density lipoprotein; SGB: Stochastic gradient boosting; LightGBM: Light Gradient Boosting Machine; CATboost: Categorical Features + Gradient Boosting. Model 3: Model 2 + inflammation factors.