Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory79.3 B

Variable types

Text1
DateTime2
Numeric6

Dataset

Description당뇨병 환자들이 시행한 혈액 검사 결과를 이용하여 공존질환과의 관련성을 평가할 수 있는 검사 데이터를 포함함. 검사 항목은 HbA1c, TG, HDL, LDL로 신장병증, 망막병증, 심근경색, 백내장과 혈관성 질환의 평가가 가능함. - HbA1c(당화혈색소): 혈액 속 적혈구 내 혈색소에 포도당 일부가 결합한 상태. 일반 혈당 검사가 검사 시점 혈당만을 알 수 있는데 반해 당화혈색소를 통해 3개월 간의 평균 혈당을 알 수 있음 -Total Cholesterol(TC, 총콜레스테롤) : 혈액 내에 있는 모든 콜레스테롤을 뜻함 - Triglyceride(TG, 중성지방): 혈 중 트리글리세라이드의 양을 측정. 혈 중 트리글리세라이드가 증가하는 이유는 분명하지 않으나 심혈관 질환으로 진행될 위험의 증가와 관련이 있음 - LDL(Low Density Lipoprotein Cholesterol): 나쁜 콜레스테롤이라고도 불리는 저밀도 지단백 콜레스테롤. 신체 콜레스테롤의 대부분을 차지하며 수치가 높으면 심장질환 및 뇌놀중 위험이 높아짐 - HDL(High Density Lipoprotein Cholesterol): 좋은 콜레스테롤이라고도 불리는 고밀도 지단백 콜레스테롤로 콜레스테롤을 흡수하여 간으로 다시 운반함. 높은 HDL cholesterol은 심장질환과 뇌졸중 위험을 낮출 수 있음
Author가톨릭대학교 서울성모병원
URLhttp://cmcdata.net/data/dataset/diabetes_coexlab

Alerts

A1C_VAL is highly overall correlated with A1C_VAL_CHigh correlation
A1C_VAL_C is highly overall correlated with A1C_VALHigh correlation
TC_VAL is highly overall correlated with LDL_VALHigh correlation
LDL_VAL is highly overall correlated with TC_VALHigh correlation
RID has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:56:11.458644
Analysis finished2023-10-08 18:56:18.776097
Duration7.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-10-09T03:56:19.060995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowR0000001
2nd rowR0000002
3rd rowR0000003
4th rowR0000004
5th rowR0000005
ValueCountFrequency (%)
r0000001 1
 
1.0%
r0000063 1
 
1.0%
r0000074 1
 
1.0%
r0000073 1
 
1.0%
r0000072 1
 
1.0%
r0000071 1
 
1.0%
r0000070 1
 
1.0%
r0000069 1
 
1.0%
r0000068 1
 
1.0%
r0000067 1
 
1.0%
Other values (90) 90
90.0%
2023-10-09T03:56:19.563482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 519
64.9%
R 100
 
12.5%
1 21
 
2.6%
3 20
 
2.5%
4 20
 
2.5%
5 20
 
2.5%
6 20
 
2.5%
7 20
 
2.5%
8 20
 
2.5%
9 20
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
87.5%
Uppercase Letter 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 519
74.1%
1 21
 
3.0%
3 20
 
2.9%
4 20
 
2.9%
5 20
 
2.9%
6 20
 
2.9%
7 20
 
2.9%
8 20
 
2.9%
9 20
 
2.9%
2 20
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
R 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
87.5%
Latin 100
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 519
74.1%
1 21
 
3.0%
3 20
 
2.9%
4 20
 
2.9%
5 20
 
2.9%
6 20
 
2.9%
7 20
 
2.9%
8 20
 
2.9%
9 20
 
2.9%
2 20
 
2.9%
Latin
ValueCountFrequency (%)
R 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 519
64.9%
R 100
 
12.5%
1 21
 
2.6%
3 20
 
2.5%
4 20
 
2.5%
5 20
 
2.5%
6 20
 
2.5%
7 20
 
2.5%
8 20
 
2.5%
9 20
 
2.5%

A1C_DATE
Date

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2009-06-01 00:00:00
Maximum2019-05-01 00:00:00
2023-10-09T03:56:19.773730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:20.047721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

A1C_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.963
Minimum5.5
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:20.628549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile5.795
Q16.475
median7.35
Q38.925
95-th percentile12.105
Maximum17.6
Range12.1
Interquartile range (IQR)2.45

Descriptive statistics

Standard deviation2.1123808
Coefficient of variation (CV)0.26527449
Kurtosis3.4973665
Mean7.963
Median Absolute Deviation (MAD)1.1
Skewness1.5920068
Sum796.3
Variance4.4621525
MonotonicityNot monotonic
2023-10-09T03:56:20.979833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.0 6
 
6.0%
6.1 5
 
5.0%
6.2 4
 
4.0%
8.0 4
 
4.0%
6.8 4
 
4.0%
6.5 4
 
4.0%
7.4 3
 
3.0%
5.9 3
 
3.0%
7.7 3
 
3.0%
7.6 3
 
3.0%
Other values (40) 61
61.0%
ValueCountFrequency (%)
5.5 2
 
2.0%
5.6 2
 
2.0%
5.7 1
 
1.0%
5.8 1
 
1.0%
5.9 3
3.0%
6.0 2
 
2.0%
6.1 5
5.0%
6.2 4
4.0%
6.3 2
 
2.0%
6.4 3
3.0%
ValueCountFrequency (%)
17.6 1
 
1.0%
13.0 1
 
1.0%
12.8 1
 
1.0%
12.3 1
 
1.0%
12.2 1
 
1.0%
12.1 1
 
1.0%
11.9 1
 
1.0%
11.6 1
 
1.0%
11.3 1
 
1.0%
10.9 3
3.0%

A1C_VAL_C
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.02
Minimum6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:21.347255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q16.75
median7
Q39
95-th percentile12
Maximum18
Range12
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation2.1224152
Coefficient of variation (CV)0.2646403
Kurtosis4.0078624
Mean8.02
Median Absolute Deviation (MAD)1
Skewness1.6551006
Sum802
Variance4.5046465
MonotonicityNot monotonic
2023-10-09T03:56:21.634666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 28
28.0%
6 25
25.0%
8 17
17.0%
9 10
 
10.0%
10 6
 
6.0%
11 6
 
6.0%
12 5
 
5.0%
13 2
 
2.0%
18 1
 
1.0%
ValueCountFrequency (%)
6 25
25.0%
7 28
28.0%
8 17
17.0%
9 10
 
10.0%
10 6
 
6.0%
11 6
 
6.0%
12 5
 
5.0%
13 2
 
2.0%
18 1
 
1.0%
ValueCountFrequency (%)
18 1
 
1.0%
13 2
 
2.0%
12 5
 
5.0%
11 6
 
6.0%
10 6
 
6.0%
9 10
 
10.0%
8 17
17.0%
7 28
28.0%
6 25
25.0%

TC/TG/HDL/LDL_DATE
Date

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2009-06-01 00:00:00
Maximum2019-05-01 00:00:00
2023-10-09T03:56:21.892110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:22.261032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TC_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.99
Minimum74
Maximum286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:22.605172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile101.9
Q1141.75
median166.5
Q3204
95-th percentile247.15
Maximum286
Range212
Interquartile range (IQR)62.25

Descriptive statistics

Standard deviation45.512457
Coefficient of variation (CV)0.26158088
Kurtosis-0.50474156
Mean173.99
Median Absolute Deviation (MAD)31.5
Skewness0.17659274
Sum17399
Variance2071.3837
MonotonicityNot monotonic
2023-10-09T03:56:22.892318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164 4
 
4.0%
225 3
 
3.0%
151 3
 
3.0%
169 2
 
2.0%
133 2
 
2.0%
214 2
 
2.0%
165 2
 
2.0%
156 2
 
2.0%
186 2
 
2.0%
174 2
 
2.0%
Other values (65) 76
76.0%
ValueCountFrequency (%)
74 1
1.0%
90 2
2.0%
97 1
1.0%
100 1
1.0%
102 1
1.0%
108 1
1.0%
110 1
1.0%
111 1
1.0%
115 1
1.0%
116 1
1.0%
ValueCountFrequency (%)
286 1
1.0%
270 1
1.0%
263 1
1.0%
257 1
1.0%
250 1
1.0%
247 1
1.0%
244 1
1.0%
243 2
2.0%
242 1
1.0%
238 1
1.0%

TG_VAL
Real number (ℝ)

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.39
Minimum34
Maximum813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:23.160699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile51.7
Q185.5
median134
Q3206.5
95-th percentile495.2
Maximum813
Range779
Interquartile range (IQR)121

Descriptive statistics

Standard deviation144.3631
Coefficient of variation (CV)0.83259185
Kurtosis5.7712173
Mean173.39
Median Absolute Deviation (MAD)58.5
Skewness2.2561405
Sum17339
Variance20840.705
MonotonicityNot monotonic
2023-10-09T03:56:23.471732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 3
 
3.0%
98 3
 
3.0%
97 3
 
3.0%
52 2
 
2.0%
102 2
 
2.0%
77 2
 
2.0%
34 2
 
2.0%
58 2
 
2.0%
164 2
 
2.0%
283 2
 
2.0%
Other values (73) 77
77.0%
ValueCountFrequency (%)
34 2
2.0%
37 1
1.0%
43 1
1.0%
46 1
1.0%
52 2
2.0%
53 2
2.0%
58 2
2.0%
62 1
1.0%
64 1
1.0%
65 1
1.0%
ValueCountFrequency (%)
813 1
1.0%
712 1
1.0%
620 1
1.0%
555 1
1.0%
518 1
1.0%
494 1
1.0%
457 1
1.0%
451 1
1.0%
389 1
1.0%
328 1
1.0%

HDL_VAL
Real number (ℝ)

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.83
Minimum23
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:23.749352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile26.95
Q137
median43
Q352
95-th percentile68
Maximum89
Range66
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.378752
Coefficient of variation (CV)0.29192128
Kurtosis1.2671043
Mean45.83
Median Absolute Deviation (MAD)8
Skewness0.97429211
Sum4583
Variance178.99101
MonotonicityNot monotonic
2023-10-09T03:56:24.101030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
41 5
 
5.0%
33 5
 
5.0%
42 5
 
5.0%
40 5
 
5.0%
43 4
 
4.0%
47 4
 
4.0%
32 4
 
4.0%
60 4
 
4.0%
45 4
 
4.0%
37 4
 
4.0%
Other values (32) 56
56.0%
ValueCountFrequency (%)
23 1
 
1.0%
24 1
 
1.0%
25 1
 
1.0%
26 2
 
2.0%
27 1
 
1.0%
30 2
 
2.0%
31 1
 
1.0%
32 4
4.0%
33 5
5.0%
34 3
3.0%
ValueCountFrequency (%)
89 1
 
1.0%
86 2
2.0%
79 1
 
1.0%
68 2
2.0%
67 1
 
1.0%
64 2
2.0%
63 1
 
1.0%
61 2
2.0%
60 4
4.0%
59 2
2.0%

LDL_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97
Minimum25
Maximum189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:24.339936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile45.65
Q166.75
median89
Q3123.5
95-th percentile165.1
Maximum189
Range164
Interquartile range (IQR)56.75

Descriptive statistics

Standard deviation38.654648
Coefficient of variation (CV)0.39850153
Kurtosis-0.71311439
Mean97
Median Absolute Deviation (MAD)30
Skewness0.35482674
Sum9700
Variance1494.1818
MonotonicityNot monotonic
2023-10-09T03:56:24.658515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123 3
 
3.0%
97 3
 
3.0%
89 3
 
3.0%
85 3
 
3.0%
78 3
 
3.0%
46 3
 
3.0%
71 3
 
3.0%
100 2
 
2.0%
60 2
 
2.0%
81 2
 
2.0%
Other values (62) 73
73.0%
ValueCountFrequency (%)
25 1
 
1.0%
28 1
 
1.0%
37 1
 
1.0%
39 2
2.0%
46 3
3.0%
47 1
 
1.0%
48 1
 
1.0%
51 1
 
1.0%
52 1
 
1.0%
54 1
 
1.0%
ValueCountFrequency (%)
189 1
1.0%
178 1
1.0%
176 1
1.0%
170 1
1.0%
167 1
1.0%
165 1
1.0%
163 1
1.0%
157 1
1.0%
153 1
1.0%
152 2
2.0%

Interactions

2023-10-09T03:56:17.743790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:12.425711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:13.557247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:14.663139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:15.652745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:16.902541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.900001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:12.597636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:13.724183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:14.849454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:15.827442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.036796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.021464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:12.824755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:13.904566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:15.019450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:16.082926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.176556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.152582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:13.041874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:14.060925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:15.200578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:16.228205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.312583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.277095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:13.229925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:14.281793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:15.356938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:16.530855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.484062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.384897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:13.393504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:14.425230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:15.513593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:16.748143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.610702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:56:24.829373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDA1C_DATEA1C_VALA1C_VAL_CTC/TG/HDL/LDL_DATETC_VALTG_VALHDL_VALLDL_VAL
RID1.0001.0001.0001.0001.0001.0001.0001.0001.000
A1C_DATE1.0001.0000.7790.8130.9990.7560.7060.0000.554
A1C_VAL1.0000.7791.0000.9280.8900.1150.3780.3360.000
A1C_VAL_C1.0000.8130.9281.0000.9000.0000.4060.3620.192
TC/TG/HDL/LDL_DATE1.0000.9990.8900.9001.0000.5160.0000.0000.000
TC_VAL1.0000.7560.1150.0000.5161.0000.1700.3360.888
TG_VAL1.0000.7060.3780.4060.0000.1701.0000.0000.000
HDL_VAL1.0000.0000.3360.3620.0000.3360.0001.0000.058
LDL_VAL1.0000.5540.0000.1920.0000.8880.0000.0581.000
2023-10-09T03:56:25.036229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A1C_VALA1C_VAL_CTC_VALTG_VALHDL_VALLDL_VAL
A1C_VAL1.0000.9780.2020.217-0.1100.213
A1C_VAL_C0.9781.0000.2000.236-0.1230.219
TC_VAL0.2020.2001.0000.3460.1950.863
TG_VAL0.2170.2360.3461.000-0.3920.156
HDL_VAL-0.110-0.1230.195-0.3921.0000.071
LDL_VAL0.2130.2190.8630.1560.0711.000

Missing values

2023-10-09T03:56:18.540532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:56:18.712281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RIDA1C_DATEA1C_VALA1C_VAL_CTC/TG/HDL/LDL_DATETC_VALTG_VALHDL_VALLDL_VAL
0R00000012009-096.462009-09126435064
1R00000022011-1110.1102011-121441114182
2R00000032009-126.672009-121939548129
3R00000042017-0612.1122017-061412753856
4R00000052009-076.872009-07143876856
5R00000062015-0712.8132015-071999152123
6R00000072017-088.992017-0819715440131
7R00000082015-129.8102015-121642083797
8R00000092010-0810.9112010-09164535989
9R00000102015-127.582015-111181125147
RIDA1C_DATEA1C_VALA1C_VAL_CTC/TG/HDL/LDL_DATETC_VALTG_VALHDL_VALLDL_VAL
90R00000912016-048.082016-0418319743108
91R00000922009-097.072009-091653452102
92R00000932015-1110.7112015-1125710058176
93R00000942013-0617.6182013-062329786122
94R00000952019-029.7102019-021591096079
95R00000962016-127.072016-1274662437
96R00000972014-129.092014-1122528336150
97R00000982016-025.962016-02110523366
98R00000992015-109.492015-101451386457
99R00001002013-107.072013-1019810248134