Age

Fig.1 Plot of Age distribution across Groups

The Dodged bar chart above represents individual counts representing frequency of age_grp categories 40-50,30-40,50-60,60-70,20-30 and 80-90 in categories cases and control . Subgroup 40-50 has highest percentage 26/80 ( 32.5 % ) in Diabetic group . Subgroup 50-60 has highest percentage 11/20 ( 55 % ) in Control group . To formally check for association between groups we performed pearson chi-square test .

we found a Non-significant association between age_grp and Group(Cases and Control) . The chi-square statistic was 7.38 . The degree of freedom was 5 and P value was 0.19 .Contingency and Proportion table are shown below

Table 4

Group	age_grp	n	value	95 % Confidence Interval
cases	20-30	2	2/80 ( 2.5 %)	0.52% - 7.78%
cases	30-40	21	21/80 ( 26.25 %)	17.57% - 36.61%
cases	40-50	26	26/80 ( 32.5 %)	23% - 43.24%
cases	50-60	22	22/80 ( 27.5 %)	18.64% - 37.96%
cases	60-70	8	8/80 ( 10 %)	4.84% - 17.98%
cases	80-90	1	1/80 ( 1.25 %)	0.14% - 5.69%
control	20-30	1	1/20 ( 5 %)	0.54% - 21.08%
control	30-40	3	3/20 ( 15 %)	4.41% - 34.86%
control	40-50	5	5/20 ( 25 %)	10.24% - 46.42%
control	50-60	11	11/20 ( 55 %)	33.77% - 74.9%

Table 5

	cases	control
20-30	2	1
30-40	21	3
40-50	26	5
50-60	22	11
60-70	8	0
80-90	1	0

Gender

Figure 2 Sex Distribution in Our Population

The Dodged bar chart above represents individual counts representing frequency of Sex categories MALE and FEMALE in categories cases and control belonging to group Group. Subgroup FEMALE has highest percentage 50/80 ( 62.5 % ) in group cases . Subgroup FEMALE has highest percentage 12/20 ( 60 % ) in group control . To formally check for association between groups we performed pearson chi-square test .

we found a Non-significant association between Sex and Group. The chi-square statistic was 0 . The degree of freedom was 1 and P value was 1 .Contingency and Proportion table are shown below

Table 6

Group	Sex	n	value	95 % Confidence Interval
cases	FEMALE	50	50/80 ( 62.5 %)	51.6% - 72.51%
cases	MALE	30	30/80 ( 37.5 %)	27.49% - 48.4%
control	FEMALE	12	12/20 ( 60 %)	38.39% - 78.94%
control	MALE	8	8/20 ( 40 %)	21.06% - 61.61%

Table 7

	cases	control
FEMALE	50	12
MALE	30	8

Smoker

Figure 3 Distribution Of smokers in Our Population

The Dodged bar chart above represents individual counts representing frequency of Smoking categories Smoker and Non-Smoker in categories cases and control . Subgroup Non-Smoker has highest percentage 65/80 ( 81.25 % ) in group cases . Subgroup Non-Smoker has highest percentage 15/20 ( 75 % ) in group control . To formally check for association between groups we performed pearson chi-square test .

we found a Non-significant association between Smoking and Group. The chi-square statistic was 0.1 . The degree of freedom was 1 and P value was 0.75 .Contingency and Proportion table are shown below

Table 8

Group	Smoking	n	value	95 % Confidence Interval
cases	Non-Smoker	65	65/80 ( 81.25 %)	71.67% - 88.61%
cases	Smoker	15	15/80 ( 18.75 %)	11.39% - 28.33%
control	Non-Smoker	15	15/20 ( 75 %)	53.58% - 89.76%
control	Smoker	5	5/20 ( 25 %)	10.24% - 46.42%

Table 9

	cases	control
Non-Smoker	65	15
Smoker	15	5

Figure 3A Distribution Of PAD in Our Population

The Dodged bar chart above represents individual counts representing frequency of PAD categories PAD and NOT PAD in categories cases and control. Subgroup NOT PAD has highest percentage 48/80 ( 60 % ) in group cases . Subgroup NOT PAD has highest percentage 19/20 ( 95 % ) in group control . To formally check for association between groups we performed pearson chi-square test .

we found a Significant association between PAD and Group. The chi-square statistic was 7.35 . The degree of freedom was 1 and P value was 0.01 .Contingency and Proportion table are shown below

Table 8A

Group	PAD	n	value	95 % Confidence Interval
cases	NOT PAD	48	48/80 ( 60 %)	49.07% - 70.22%
cases	PAD	32	32/80 ( 40 %)	29.78% - 50.93%
control	NOT PAD	19	19/20 ( 95 %)	78.92% - 99.46%
control	PAD	1	1/20 ( 5 %)	0.54% - 21.08%

Table 9A

	cases	control
NOT PAD	48	19
PAD	32	1

ABPI

Figure 4 Boxplot Of Distribution Of ABPI in our Population

In this Figure we see Box plot of ABPI in : cases and control respectively .The individual jittered data points of ABPI are overlaid over transparent Boxplot for better visualisation. We see distribution of data in individual sub-groups based on these box-plots. The lower edge of box plot represents -first quartile (Q1), Horizontal bar represents the median, Upper edge represnts third quartile (Q3), Two black lines (whiskers) emanating from box-plots signify range of non-outlier data for the particular sub-group. Lower whisker represents minimum(Q1- 1.5 interquartile range) non-outlier limit of ABPI and upper whisker represnts maximum(Q1+1.5interquartile range) of ABPI .Any data beyond whiskers of box-plots represents outliers in the sub-groups The big brown point in the box-plots represents mean ABPI of 2 groups and it has been annotated in the figure itself Summary Statistics of the groups is presented in table below

Table 10 Summary Table Of ABPI within Groups

Group	n	Mean	SD	Median	Minimum	Maximum
cases	80	0.922	0.093	0.920	0.69	1.14
control	20	1.131	0.095	1.135	0.89	1.32

The mean in Group cases [ 0.92 ± 0.09 ] was significantly lower than Group control [ 1.13 ± 0.09 ] . The mean difference was -0.21 and 95 % confidence interval of the difference was ( -0.26 - -0.16 ) . The p value was <0.001 . The t statistic was -8.88 and degree of freedom of the Welch unpaired two-sample t test was 28.84 .In Formal statistical notation this result is expressed as : t(28.84) = -8.88, p= <0.001.. The detailed statistical parameters of T test are given in table below.

TABLE 11

variable	group1	group2	statistic	df	p
ABPI	cases	control	-8.88	28.84	<0.001

Figure 5 Barplot Of Age-Sex Distribution Of ABPI in our Population

Table 12 Age-Sex Distribution Of ABPI in our Population

Group	age_grp	Sex	n	Mean ( ABPI )	SD ( ABPI )	Median ( ABPI )
cases	20-30	FEMALE	1	0.99		0.99
cases	20-30	MALE	1	0.90		0.90
cases	30-40	FEMALE	14	0.92	0.13	0.92
cases	30-40	MALE	7	0.91	0.08	0.91
cases	40-50	FEMALE	12	0.89	0.08	0.88
cases	40-50	MALE	14	0.89	0.07	0.88
cases	50-60	FEMALE	17	0.95	0.1	0.94
cases	50-60	MALE	5	0.94	0.07	0.93
cases	60-70	FEMALE	5	0.93	0.08	0.96
cases	60-70	MALE	3	1.03	0.04	1.04
cases	80-90	FEMALE	1	0.85		0.85
control	20-30	FEMALE	1	1.13		1.13
control	30-40	FEMALE	1	1.15		1.15
control	30-40	MALE	2	1.12	0.12	1.12
control	40-50	FEMALE	3	1.06	0.16	1.08
control	40-50	MALE	2	1.17	0.04	1.17
control	50-60	FEMALE	7	1.17	0.09	1.14
control	50-60	MALE	4	1.10	0.09	1.07

CRP

Figure 6 Boxplot Of Distribution Of CRP in our Population

In this Figure we see Box plot of CRP in 2 sub-groups : cases and control respectively .The individual jittered data points of CRP are overlaid over transparent Boxplot for better visualisation. We see distribution of data in individual sub-groups of Group based on these box-plots. The lower edge of box plot represents -first quartile (Q1), Horizontal bar represents the median, Upper edge represnts third quartile (Q3), Two black lines (whiskers) emanating from box-plots signify range of non-outlier data for the particular sub-group. Lower whisker represents minimum(Q1- 1.5 interquartile range) non-outlier limit of CRP and upper whisker represnts maximum(Q1+1.5interquartile range) of CRP .Any data beyond whiskers of box-plots represents outliers in the sub-groups The big brown point in the box-plots represents mean CRP of 2 groups and it has been annotated in the figure itself We can see the statistical summary of Test while summary statistics of Effect size and its confidence Interval (represented as Hedges’s G ) at top of plot. Summary Statistics of the groups is presented in table below

Table 13 Summary Table Of CRP within Groups

Group	n	Mean	SD	Median	Minimum	Maximum
cases	80	8.000	3.001	7.925	1.80	16.15
control	20	2.099	0.699	1.965	1.07	3.53

The mean in Group cases [ 8 ± 3.0 ] was significantly higher than Group control [ 2.1 ± 0.7 ] . The mean difference was 5.9 and 95 % confidence interval of the difference was ( 5.17 - 6.64 ) . The p value was <0.001 . The t statistic was 15.94 and degree of freedom of the Welch unpaired two-sample t test was 97.85 .In Formal statistical notation this result is expressed as : t(97.85) = 15.94, p= <0.001.

TABLE 14

variable	group1	group2	statistic	df	p
CRP	cases	control	15.94	97.85	<0.001

Figure 5 Barplot Of Age-Sex Distribution Of CRP in our Population

Table 15 Age-Sex Distribution Of CRP in our Population

Group	age_grp	Sex	n	Mean ( CRP )	SD ( CRP )	Median ( CRP )
cases	20-30	FEMALE	1	12.40		12.40
cases	20-30	MALE	1	5.30		5.30
cases	30-40	FEMALE	14	7.74	3.5	6.76
cases	30-40	MALE	7	8.64	2.19	9.76
cases	40-50	FEMALE	12	7.62	3.18	7.89
cases	40-50	MALE	14	8.17	3.28	8.73
cases	50-60	FEMALE	17	8.22	3.39	7.45
cases	50-60	MALE	5	6.45	1.44	7.24
cases	60-70	FEMALE	5	8.93	2.52	9.48
cases	60-70	MALE	3	8.13	1.34	8.20
cases	80-90	FEMALE	1	6.60		6.60
control	20-30	FEMALE	1	2.04		2.04
control	30-40	FEMALE	1	1.98		1.98
control	30-40	MALE	2	2.74	1.12	2.74
control	40-50	FEMALE	3	1.96	1.08	1.64
control	40-50	MALE	2	2.37	0.14	2.37
control	50-60	FEMALE	7	2.20	0.75	2.24
control	50-60	MALE	4	1.62	0.28	1.60

Selected Correlations

ABPI and CRP

Figure showing Correlation between ABPI and CRP

The scatter plots above show relationship between CRP on X axis and ABPI on Y axis. Graphically, we see that as CRP increases, ABPI decreases . On a formal statistical linear regression analysis, we that line of best fit (blue line signifying line with least square difference) also has a negative slope implying a negative correlation. The gray shaded error around blue line signifies 95% confidence interval of linear regression line of best fit. The correlation between two variables is Significant . The Pearson’s correlation between CRP and ABPI is -0.26 with 95% Confidence Interval of -0.43 to -0.07. the t statistic is -2.65 The p value is 0.01 .The degree of freedom is 98. In formal statistical notation this expressed as t(98)= -2.65, P= 0.01. r(Pearson) = -0.26 95% C.I. [-0.43–0.07]. n= 100. The correlation is summmarised in table below

Table 16. Table Summarizing correlation between CRP and ABPI

Group 1	Group 2	Degree of Freedom	T statistic	Correlation	95 % C.I.	P value
CRP	ABPI	98	-2.65	-0.26	-0.43–0.07	0.01

Table 16 Table with summary statistics of CRP and ABPI

variable	n	Mean	SD	Median	Minimum	Maximum
ABPI	100	0.964	0.125	0.945	0.69	1.32
CRP	100	6.820	3.592	6.915	1.07	16.15

ABPI and HBA1c**

Figure showing Correlation between ABPI and HBA1C

he scatter plots above show relationship between HBA1C on X axis and ABPI on Y axis. Graphically, we see that as HBA1C increases, ABPI decreases . On a formal statistical linear regression analysis, we that line of best fit (blue line signifying line with least square difference) also has a negative slope implying a negative correlation. The gray shaded error around blue line signifies 95% confidence interval of linear regression line of best fit. The correlation between two variables is Significant . The Pearson’s correlation between HBA1C and ABPI is -0.56 with 95% Confidence Interval of -0.68 to -0.41. the t statistic is -6.77 The p value is <0.001 .The degree of freedom is 98. In formal statistical notation this expressed as t(98)= -6.77, P= <0.001. r(Pearson) = -0.56 95% C.I. [-0.68–0.41]. n= 100. The correlation is summmarised in table below

Table 17. Table Summarizing correlation between ABPI and HBA1c

Group 1	Group 2	Degree of Freedom	T statistic	Correlation	95 % C.I.	P value
HBA1C	ABPI	98	-6.77	-0.56	-0.68–0.41	<0.001

Table 18 Table with summary statistics of ABPI and HBA1C

variable	n	Mean	SD	Median	Minimum	Maximum
ABPI	100	0.964	0.125	0.945	0.690	1.320
HBA1C	100	7.360	0.739	7.628	5.625	8.577

ABPI AND LDL

Figure showing Correlation between ABPI and LDL

The scatter plots above show relationship between LDL on X axis and ABPI on Y axis. Graphically, we see that as LDL increases, ABPI decreases . On a formal statistical linear regression analysis, we that line of best fit (blue line signifying line with least square difference) also has a negative slope implying a negative correlation. The gray shaded error around blue line signifies 95% confidence interval of linear regression line of best fit. The correlation between two variables is Significant . The Pearson’s correlation between LDL and ABPI is -0.54 with 95% Confidence Interval of -0.67 to -0.39. the t statistic is -6.38 The p value is <0.001 .The degree of freedom is 98. In formal statistical notation this expressed as t(98)= -6.38, P= <0.001. r(Pearson) = -0.54 95% C.I. [-0.67–0.39]. n= 100. The correlation is summmarised in table below

Table 19. Table Summarizing correlation between ABPI and LDL

Group 1	Group 2	Degree of Freedom	T statistic	Correlation	95 % C.I.	P value
ABPI	LDL	98	-6.38	-0.54	-0.67–0.39	<0.001

Table 20 Table with summary statistics of ABPI and LDL

variable	n	Mean	SD	Median	Minimum	Maximum
ABPI	100	0.964	0.125	0.945	0.69	1.32
LDL	100	152.000	29.671	162.000	77.00	199.00

ABPI and 10 yr ASCVD Risk Score

Figure showing Correlation between ABPI and ASCVD

The scatter plots above show relationship between ASCVD on X axis and ABPI on Y axis. Graphically, we see that as ASCVD increases, ABPI decreases . On a formal statistical linear regression analysis, we that line of best fit (blue line signifying line with least square difference) also has a negative slope implying a negative correlation. The gray shaded error around blue line signifies 95% confidence interval of linear regression line of best fit. The correlation between two variables is Significant . The Pearson’s correlation between ASCVD and ABPI is -0.55 with 95% Confidence Interval of -0.68 to -0.4. the t statistic is -6.58 The p value is <0.001 .The degree of freedom is 98. In formal statistical notation this expressed as t(98)= -6.58, P= <0.001. r(Pearson) = -0.55 95% C.I. [-0.68–0.4]. n= 100. The correlation is summmarised in table below

Table 21. Table Summarizing correlation between ABPI and ASCVD

Group 1	Group 2	Degree of Freedom	T statistic	Correlation	95 % C.I.	P value
ABPI	ASCVD	98	-6.58	-0.55	-0.68–0.4	<0.001

Table 22 Table with summary statistics of ASCVD and ABPI

variable	n	Mean	SD	Median	Minimum	Maximum
ABPI	100	0.964	0.125	0.945	0.69	1.32
ASCVD	100	7.840	2.498	8.660	2.78	11.75

Correlation Matrix Of selected Variables

Table 23 Correlation table of Selected variables TSH,LDL,T3,T4,TC,HDL,LDL,TG,LipoproteinA,ASCVD,HbA1c with their confidence intervals

Variable1	Variable2	Correlation	pvalue	significance	Confidence_Interval
LDL	Total_Cholesterol	0.99	<0.001	Significant	0.98-0.99
TG	HBA1C	0.93	<0.001	Significant	0.89-0.95
LDL	HBA1C	0.92	<0.001	Significant	0.89-0.95
TG	Total_Cholesterol	0.92	<0.001	Significant	0.88-0.95
Total_Cholesterol	HBA1C	0.92	<0.001	Significant	0.88-0.94
LDL	TG	0.90	<0.001	Significant	0.85-0.93
ASCVD	HBA1C	0.87	<0.001	Significant	0.81-0.91
SBP	DBP	0.86	<0.001	Significant	0.8-0.9
ASCVD	Total_Cholesterol	0.84	<0.001	Significant	0.77-0.89
ASCVD	TG	0.84	<0.001	Significant	0.76-0.89
ASCVD	LDL	0.83	<0.001	Significant	0.76-0.88
HDL	HBA1C	-0.83	<0.001	Significant	-0.88–0.75
LDL	HDL	-0.79	<0.001	Significant	-0.86–0.71
HDL	TG	-0.79	<0.001	Significant	-0.85–0.7
DBP	HBA1C	0.78	<0.001	Significant	0.69-0.85
DBP	LDL	0.76	<0.001	Significant	0.66-0.83
DBP	Total_Cholesterol	0.76	<0.001	Significant	0.66-0.83
DBP	TG	0.74	<0.001	Significant	0.63-0.81
HDL	Total_Cholesterol	-0.72	<0.001	Significant	-0.81–0.61
ASCVD	DBP	0.70	<0.001	Significant	0.58-0.79
ASCVD	HDL	-0.67	<0.001	Significant	-0.77–0.55
SBP	HBA1C	0.67	<0.001	Significant	0.54-0.76
CRP	Total_Cholesterol	0.65	<0.001	Significant	0.52-0.75
CRP	LDL	0.65	<0.001	Significant	0.51-0.75
CRP	ASCVD	0.64	<0.001	Significant	0.51-0.74
CRP	HBA1C	0.64	<0.001	Significant	0.5-0.74
DBP	HDL	-0.63	<0.001	Significant	-0.74–0.5
SBP	Total_Cholesterol	0.61	<0.001	Significant	0.47-0.72
SBP	LDL	0.61	<0.001	Significant	0.47-0.72
SBP	TG	0.61	<0.001	Significant	0.47-0.72
CRP	TG	0.61	<0.001	Significant	0.47-0.72
ASCVD	SBP	0.59	<0.001	Significant	0.45-0.71
CRP	DBP	0.57	<0.001	Significant	0.42-0.69
ABPI	HBA1C	-0.56	<0.001	Significant	-0.68–0.41
ABPI	ASCVD	-0.55	<0.001	Significant	-0.68–0.4
ABPI	Total_Cholesterol	-0.55	<0.001	Significant	-0.67–0.39
ABPI	LDL	-0.54	<0.001	Significant	-0.67–0.39
CRP	SBP	0.53	<0.001	Significant	0.37-0.66
SBP	HDL	-0.51	<0.001	Significant	-0.65–0.35
ABPI	TG	-0.50	<0.001	Significant	-0.63–0.34
CRP	HDL	-0.50	<0.001	Significant	-0.63–0.33
ABPI	DBP	-0.46	<0.001	Significant	-0.61–0.3
ABPI	SBP	-0.41	<0.001	Significant	-0.56–0.23
ABPI	HDL	0.39	<0.001	Significant	0.21-0.55
ABPI	CRP	-0.26	0.00945	Significant	-0.43–0.07

Linear regression model

Multiple linear regression was conducted to find best combination of HBA1C,Age,SexMALE,LDL,TG & CRP for predicting ABPI . Dummy indicator(0/1) were used for categorical variables. The Forest plot above shows standardized regression coefficients of HBA1C,Age,SexMALE,LDL,TG & CRP with their confidence intervals as horizontal error bars on X axis. An error bar which crosses vertical line of zero in this plot is non-significant.

We fitted a linear model (estimated using OLS) to predict ABPI with HBA1C, Age, Sex, LDL, TG and CRP (formula = ABPI ~ HBA1C + Age + Sex + LDL + TG + CRP). Standardized parameters were obtained by fitting the model on a standardized version of the dataset. Effect sizes were labelled following Funder's (2019) recommendations.



The model explains a significant and substantial proportion of variance (R2 = 0.36, F(6, 93) = 8.86, p < .001, adj. R2 = 0.32). The model's intercept, corresponding to ABPI = 0, HBA1C = 0, Age = 0, Sex = , LDL = 0, TG = 0 and CRP = 0, is at 1.66 (SE = 0.19, 95% CI [1.29, 2.03[, std. intercept = 0.02, p < .001). Within this model:

  - The effect of HBA1C is negative and can be considered as medium and significant (beta = -0.10, SE = 0.04, 95% CI [-0.19, -0.01[, std. beta = -0.60, p < .05).
  - The effect of Age is positive and can be considered as very small and not significant (beta = 0.00, SE = 0.00, 95% CI [0.00, 0.00[, std. beta = 0.12, p > .1).
  - The effect of Sex (MALE) is negative and can be considered as tiny and not significant (beta = -0.01, SE = 0.02, 95% CI [-0.05, 0.04[, std. beta = -0.05, p > .1).
  - The effect of LDL is negative and can be considered as small and not significant (beta = 0.00, SE = 0.00, 95% CI [0.00, 0.00[, std. beta = -0.29, p > .1).
  - The effect of TG is positive and can be considered as very small and not significant (beta = 0.00, SE = 0.00, 95% CI [0.00, 0.00[, std. beta = 0.19, p > .1).
  - The effect of CRP is positive and can be considered as very small and not significant (beta = 0.01, SE = 0.00, 95% CI [0.00, 0.01[, std. beta = 0.20, p = 0.08).

Table 24. Regression Table

Parameter	Coefficient	CI_low	CI_high	p	Std_Coefficient	Fit
(Intercept)	1.662	1.292	2.033	0	0.018
HBA1C	-0.101	-0.19	-0.012	0.026	-0.598
Age	0.002	-0.001	0.004	0.143	0.123
SexMALE	-0.006	-0.048	0.037	0.787	-0.046
LDL	-0.001	-0.003	0.001	0.213	-0.288
TG	0.001	-0.001	0.002	0.416	0.187
CRP	0.007	-0.001	0.014	0.075	0.197
R2						0.364
R2 (adj.)						0.323

The combination of these predictors significantly predicted ABPI .There were 100 observations in our model. The number of predictors in model was 6 ,while degree of freedom of residuals(no.of observation-number Of predictors in model) was 93. In statistical notation this is expressed as F(6,93) = 8.86, P = <0.001 .The standard deviation of residual error was 0.1 implying ABPI was predicted with average accuracy of +- 0.1 by our model. The adjusted R - Square for our model is 0.32 implying our model predicts 32.27 percentage variation in ABPI .

In Our Multivariable linear regression Model,On adjusting for all variables , HBA1C significantly predicted ABPI .

Our Final regression equation was predicted ABPI = 1.6 -0.1HBA1C +0.002Age -0.006SexMALE -0.001LDL +0.0001TG +0.0007CRP

Interpretation

1 unit change in HBA1C leads to 0.1 decrease in ABPI . Rest of values are not Significant.

Table 25 Univariable and Multivariable Regression coefficients

Dependent: ABPI		Mean (sd)	Coefficient (univariable)	Coefficient (multivariable)
HBA1C	[5.62,8.58]	1.0 (0.1)	-0.096 (-0.124 to -0.068, p<0.0001)	-0.101 (-0.190 to -0.012, p=0.0263)
Age	[27,82]	1.0 (0.1)	0.001 (-0.001 to 0.004, p=0.2469)	0.002 (-0.001 to 0.004, p=0.1429)
Sex	FEMALE	1.0 (0.1)
	MALE	1.0 (0.1)	-0.003 (-0.055 to 0.048, p=0.8965)	-0.006 (-0.048 to 0.037, p=0.7872)
LDL	[77,199]	1.0 (0.1)	-0.002 (-0.003 to -0.002, p<0.0001)	-0.001 (-0.003 to 0.001, p=0.2130)
TG	[101,264]	1.0 (0.1)	-0.002 (-0.002 to -0.001, p<0.0001)	0.001 (-0.001 to 0.002, p=0.4163)
CRP	[1.07,16.1]	1.0 (0.1)	-0.009 (-0.016 to -0.002, p=0.0095)	0.007 (-0.001 to 0.014, p=0.0754)