Home Blood Pressure-based Guidance Did Not Increase Anti-albuminuric Effects on Diagnostic Provision of Microalbuminuria in School Workers: A Miyagi Karoshi Prevention Study

Abstract

Objective

We examined whether home blood pressure (BP)-based behavioural guidance had an additional anti-albuminuric effect on school workers with the simple diagnostic provision of microalbuminuria.

Methods

Of 169 school staff diagnosed with microalbuminuria (30-299.9 mg/gCr) in the annual 2019 health check-up, 91 agreed to receive home-BP-based guidance. Guidance comprised, for subjects with ≥135/85 mmHg, 5 days mean of home BP measurements, or encouraging medical consultation and lifestyle guidance; lifestyle guidance for subjects with BP 125-134/80-84 mmHg; and adequate lifestyle guidance for subjects BP ＜125/80 mmHg, if necessary. The main outcome was a change in the frequency of microalbuminuria the following year. Subjects with menstruation were excluded from analysis. Finally, there were 48 and 43 participants in guided and the non-guided groups, respectively.

Results

The guided and non-guided groups demonstrated similar baseline clinical data. Their prescription rates for hypertension (39.6 vs. 41.9 %) and diabetes (18.8 vs. 30.2 %) were similar. One year later, microalbuminuria was present in 31.2% and 30.2% of the guided and non-guided groups (n.s.), respectively, suggesting a ~70% risk reduction of microalbuminuria in both groups. Sensitivity analysis, excluding patients treated for hypertension or diabetes at baseline, demonstrated essentially similar results. In conclusion, the risk reduction of microalbuminuria was nearly 70% for both the home-BP-based guidance and non-guidance groups.

Conclusion

These data suggest that home BP-based guidance did not increase anti-albuminuric effects on simple diagnostic provision of microalbuminuria in school workers.

Introduction

It has been well recognized that long working hours are risk of cardiovascular events or Karoshi (1,2). Working hours of Japanese employees during this three decades tended to decrease in many categories but opposite is true in some jobs like teachers (3). The establishment of preventive strategy of Karoshi is urgent for such long working population (4). For this purpose, targeting intervention to high risk individuals would be the most effective approach because frequency of Karoshi is not so high among general workers (4). In this regard, measurement of urinary albumin excretion (UAE) may be the most practical method to find high risk individuals with subclinical atherosclerosis in terms of cost and convenience (5-7). Increase of UAE reflects the glomerular endothelial damage and is known to be correlated with systemic small vessel damage (8). It has been reported that microalbuminuria is associated with increased risk of cardiovascular events independent of traditional risk factors in diabetes (9), hypertensive (10,11) and the general population (12-16). We have recently shown that subjects with microalbuminuria demonstrated 2.4 times higher risk of cardiovascular events as compared with normo-albuminuria in the Japanese general population (16). Accordingly, determination of microalbuminuria has been reported to facilitate the risk stratification and the choice of more adequate therapy in essential hypertensive patients (5). Moreover, treatment or lifestyle guidance according to blood pressure (BP) may further improve prognosis of albuminuria because high BP is one of the most important modifiable risk factors for albuminuria (17-19). It has been reported that home BP measurements better predict progression of organ damage and cardiovascular events than clinic measurements in hypertensive patients (20-22). In this study, therefore, we for the first time examined if home-BP-based health guidance is effective to reduce UAE as compared with non-guidance or the simple diagnostic provision in school workers identified as having microalbuminuria.

Materials and Methods

Study population

We studied 3,868 public school staffs, mainly composed of teachers (81%) who work for Miyagi prefectural schools (74 high schools, 26 special needs schools). All agreed to participate in the study and gave signed informed consent after reviewing videodisk to explain the background, purpose and method of the study. Among them, 3,718 individual received legal annual health check-up in 2019. This study was approved by the ethics committee of Tohoku Rosai Hospital and registered for UMIN (ID 000040247).

Measurements

Height, body weight, and waist circumference (WC) were measured by trained nurses. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. After 5 minutes of sitting, BP was measured using a semiautomatic cuff-oscillometric sphygmomanometer (BX-10-AAM; Colin, Japan or Vital Note 2580 A&D, Tokyo, Japan). Blood samples were collected after an overnight fast and then analyzed for low and high-density lipoprotein (LDL and HDL) cholesterol, triglycerides, HbA1c (National Glycohemoglobin Standardization Program), uric acid, and creatinine levels. HbA1c level was determined using HPLC (HLC-723 G9; Toso, Yamaguchi, Japan), and the levels of the remaining biochemical markers were determined using a standard automatic analyser (LABOSPECT; Hitachi, Tokyo, Japan). Estimated glomerular filtration rate (eGFR) was calculated using the formula provided by the Japanese Society of Nephrology: eGFR (mL/min per 1.73 m²)=194×creatinine^-1.094×age^-0.287 (×0.739 if female). Morning spot urine samples were also collected for the measurement of UAE and sodium excretion. Urinary albumin, creatinine and sodium concentrations were determined using authorized automatic analyzer (JCA-BM6050, JEOL, Tokyo, Japan). UAE was expressed as the urine albumin-to-creatinine ratio as described previously (16). Population mean of daily sodium intake was calculated by dividing 24-hour urinary sodium excretion according to Tanaka's method by 17 (23).

Information regarding past and current medical history and lifestyle factors were examined using a questionnaire of specific health examination as reported previously (24). Life style factors related to physical activity and dietary habits were scored as followings: answers of “yes” and “no” to question of “Are you in a habit of doing exercise to sweat lightly for over 30 minutes a time, 2 times weekly, for over a year?” were “1” and “0”; answers of “yes” and “no” to question of “In your daily life do you walk or do any equivalent amount of physical activity more than one hour a day?” was “1” and “0”; answers of “quicker,” “normal” and “late” to question of “Is your eating speed quicker than others ?” were “0,” ”1” and “1;” answers of “yes” and “no” to question of “Is your walking speed faster than the speed of those of your age and sex?” was “1” and “0”; answers of “yes” and “no” to question of “Do you eat supper within two hours before bedtime more than 3 times a week?” were “0” and “1”; answers of “yes” and “no” to question of “Do you eat snacks or drink sweet beverage between meals? were “0” and “1”; answers of “yes” and “no” to question of “Do you skip breakfast more than 3 times a week?” were “0” and “1.” Finally, total lifestyle score was expressed as sum of individual score ranges from 0 to 7, higher the better lifestyle.

Intervention

Results of urinary albumin excretion were categorized as normoalbuminuria (0-29.9 mg/gCr), microalbuminuria (30-299.9 mg/gCr) and macroalbuminuria (300 or more mg/gCr) and were sent to all subjects by mail. Subjects with microalbuminuria were requested to receive home-BP based guidance and those with macroalbuminuria were demanded to report the results to their family doctor or nephrologist in case of no family doctor. Risk of microalbuminuria and the contents of home-BP based guidance were well explained to subjects with microalbuminuria before they gave additional written informed consent.

Among 3,718 individuals who completed full examinations, 169 were diagnosed as having microalbuminuria. Among them, 91 agreed to receive home-BP based guidance, whereas the remaining 78 individuals did not respond to the request (Fig. 1). Agreed subjects were requested to measure BP at home twice a day for 5 days, according to Japanese hypertension guideline 2019 (25). Validated device (HEM-8713, Omron, Kyoto, Japan) was provided for all subjects. They were also asked to answer to detailed question on past and current medical history, lifestyle and dietary habit to provide individualized guidance. Guidance was given according to 5 days mean of home BP measurements, or encouraging medical consultation and lifestyle guidance for subjects with ≥135/85 mmHg, lifestyle guidance for subjects with 125-134/80-84 mmHg and adequate lifestyle guidance for subjects ＜125/80 mmHg if abnormal glucose metabolism or dyslipidemia are present. Information letter on the diagnosis of microalbuminuria was provided to subjects who need medical consultation, and those who had been already treated. Guidance was given by doctor or authorized nurse. Content of guidance was well standardized and individualized by staff meeting beforehand. Primary outcomes were change in UAE and frequency of microalbuminuria in the setting of health check-up next year.

Figure 1.

Flowchart for participant selection and categorization.

Statistical analysis

Of the 169 subjects who showed microalbuminuria at baseline, women who had menstrual period either at baseline or at follow-up were excluded from analysis because menstruation is known to overestimate urinary protein level (26). Moreover, some subjects were lost to follow-up due to several reason. Major reason was changeover to different health check-up system, in which measurement of urinary albumin excretion was not available. Moreover, some subjects were retired due to several reasons. Finally, paired measurements were obtained in 48 guided and 43 non-guided individuals. They were subjected to final analysis. Fig. 1 shows the flow of this study. Among 48 guided individuals, frequency of home-BP measurements with ≥135/85 mmHg, 125-134/80-84 mmHg and ＜125/80 mmHg were 29 (60.4%), 8 (16.7%) and 11(22.9%), respectively.

Data are expressed as mean±standard deviation or median (25th, 75th) or number (%). Group comparison was performed using the t-test and Chi-square test for continuous and categorical variables, respectively. Within group difference was examined using the paired t-test or McNemar's test. Skewed data were log-transformed before statistical analysis if necessary. Between group difference was examined by ANOVA or ANCOVA adjusted for age, sex and baseline values. All statistical analyses were performed using the JMP (version 14.0 for Windows; SAS Institute, Cary, USA) software. A p value ＜0.05 was considered to be statistically significant.

Results

Table 1 compared baseline data between guided and the non-guided groups. Age (49.7±9.3 vs. 52.9±9.9 yrs), male frequency (83.3 vs. 76.7%), BMI (26.7±5.3 vs. 26.3±4.7 kg/m²), BP, renal function, glucose and lipid profiles, and UAE (63.7 vs. 54.1 mg/gCr) did not differ significantly between the groups. Prescription rate for hypertension (39.6 vs. 41.9%) diabetes (18.8 vs. 30.2%) and dyslipidemia (22.9 vs. 25.6%) also did not differ between the groups. Lifestyle score was significantly lower in guided group as compared with non-guided group (p=0.016).

Table 1.

Baseline Characteristics in 2019.

	Non-guided group	Guided group	p value
	n=43	n=48
Age (yrs)	52.9±9.9	49.7±9.3	0.123
Men	33 (76.7)	40 (83.3)	0.445
BMI (kg/m2)	26.3±4.7	26.7±5.3	0.740
Systolic blood pressure (mmHg)	132.5±15.5	130.5±18.7	0.584
Diastolic blood pressure (mmHg)	85.2±12.1	85.6±14.7	0.889
eGFR (mL/min/1.73 m2)	73.2±14.9	70.1±15.8	0.340
HbA1c (%)	6.5±1.4	6.1±1.3	0.211
Uric acid (mg/dL)	5.8±1.3	6.1±1.4	0.223
LDL (mg/dL)	115.2±30.2	121.7±29.5	0.303
HDL (mg/dL)	53.7±10.4	55.5±14.9	0.502
Triglyceride (mg/dL)	162 (110, 199)	122 (77, 206)	0.245
Urinary albumin excretion (mg/gCr)	54.1 (36.6, 112.5)	63.7 (36.5, 114.0)	0.873
Estimated salt intake (g/day)	9.1±2.6	8.5±2.3	0.210
Medication for hypertension	18 (41.9)	19 (39.6)	0.834
Medication for diabetes	13 (30.2)	9 (18.8)	0.228
Medication for dyslipidemia	11 (25.6)	11 (22.9)	0.810
Lifestyle score	4.6±1.2	3.9±1.5	0.016

Mean±SD or median (25th, 75th), or n (%)

Fig. 2 demonstrates individual changes in UAE in guided and non-guided group. UAE was significantly lowered in both groups (p＜0.001), but one reached macro-albuminuria level in either group.

Figure 2.

Individual changes in urinary albumin excretion.

Table 2 shows the results of within-group difference in each variable and the comparison of between group difference. Log UAE was significantly lowered one year later in both groups and there was no difference in the changes between the group either in non-adjusted or adjusted model. Systolic blood pressure (SBP) was significantly lowered and HbA1c and estimated daily sodium intake was significantly increased one year later as compared with baseline in guided group. eGFR was significantly lowered one year later as compared with baseline in non-guided group. None of those changes, however, differed significantly between the groups either in non-adjusted or adjusted model.

Table 2.

Comparison of Data within Group and between Groups.

	Group	Baseline	1 year	Difference (95% CI)	Within group difference p value	Between group difference p value
						Unadjusted	Adjusted
BMI (kg/m2)	Guided	26.7±5.3	26.7±5.2	-0.00 (-0.28, 0.27)	0.971	0.915	0.875
	Non-guided	26.3±4.7	26.3±4.4	-0.03 (-0.38, 0.33)	0.917
SBP (mmHg)	Guided	130.5±18.7	126.1±18.0	-4.46 (-8.46, -0.45)	0.040	0.186	0.093
	Non-guided	132.5±15.5	132.1±14.2	-0.44 (-5.01, 4.12)	0.662
DBP (mmHg)	Guided	85.6±14.7	83.0±13.9	-2.60 (-5.22, 0.01)	0.057	0.597	0.504
	Non-guided	85.2±12.1	83.6±9.3	-1.61 (-4.36, 1.15)	0.177
eGFR (mL/min/1.73m2)	Guided	70.1±15.8	69.9±16.7	-0.23 (-2.17, 1.72)	0.727	0.291	0.599
	Non-guided	73.2±14.9	71.4±13.8	-1.81 (-4.09, 0.48)	0.036
HbA1c (%)	Guided	6.14±1.29	6.16±0.90	0.02 (-0.25, 0.30)	0.011	0.390	0.982
	Non-guided	6.50±1.43	6.33±0.84	-0.17 (-0.51, 0.18)	0.417
UA (mg/dL)	Guided	6.14±1.42	6.07±1.45	-0.07 (-0.36, 0.22)	0.821	0.128	0.258
	Non-guided	5.79±1.35	6.01±1.45	0.22 (-0.03, 0.48)	0.133
LDL (mg/dL)	Guided	121.7±29.5	119.9±27.6	-1.79 (-7.89, 4.30)	0.841	0.467	0.721
	Non-guided	115.2±30.2	116.6±28.8	1.44 (-5.07, 7.95)	0.876
HDL (mg/dL)	Guided	55.5±14.9	55.8±13.1	0.23 (-2.01, 2.46)	0.734	0.870	0.470
	Non-guided	53.7±10.4	54.2±10.2	0.49 (-1.78, 2.75)	0.313
LogTG	Guided	2.09±0.30	2.11±0.27	0.01 (-0.05, 0.07)	0.667	0.216	0.496
	Non-guided	2.16±0.25	2.12±0.21	-0.04 (-0.09, 0.02)	0.123
LogUAE	Guided	1.82±0.27	1.31±0.49	-0.51 (-0.65, -0.37)	<0.001	0.625	0.546
	Non-guided	1.83±0.28	1.27±0.52	-0.56 (-0.71, -0.41)	<0.001
Salt intake (g/day)	Guided	8.5±2.3	10.0±2.4	1.51 (0.52, 2.51)	0.005	0.232	0.622
	Non-guided	9.1±2.6	9.8±1.9	0.69 (-0.28, 1.65)	0.147

Mean±SD

Fig. 3 shows the frequency of normoalbuminuria and micro- or macroalbuminuria one year later. Thirty-three of guided group (68.8%) and 30 of non-guided group (69.8%) recovered to normoalbuminuria and the normalization rate did not differ between the groups. Life style score tended to increase in either group next year but still tended to be lower in guided than in non-guided group (4.2±1.4 vs. 4.7±1.4, p=0.058).

Figure 3.

Frequency of normoalbuminuria and micro- or macroalbuminuria in 2020.

Fig. 4 shows the changes in prescription rate for hypertension and diabetes in each group. Prescription rate for hypertension was similarly increased in 2020 as compared with 2019 in both groups. Prescription rate for diabetes increased in the non-guided group while it remained unchanged in the guided group.

Figure 4.

Prescription rate for hypertension and diabetes.

We further conducted sensitivity analysis excluding subject treated with hypertension or diabetes at baseline (Supplementary material 1, 2). Guided and non-guided groups included 28 and 19 subjects, respectively. None of the baseline data differed between the groups (Supplementary material 1, 2). UAE was significantly lowered one year later as compared with baseline in both guided (p＜0.001) and non-guided (p=0.008) groups (Supplementary material 2). Frequency of micro- or macroalbuminuria was 25.0% and 21.1% for guided and non-guided group, respectively, suggesting that 75.0 and 78.9% was recovered to normoalbuminuria in each group. UAE in 2020 was 14 and 13 mg/g∙Cr for guided and non-guided group, respectively (n.s.). Baseline lifestyle score tended to be lower in guided group than in non-guided group (4.0±1.7 vs. 4.6±1.0, p=0.095). The score tended to be still lower in guided group than in the non-guided group next year although it tended to increase in both groups (4.4±1.5 vs. 5.0±1.3, p=0.124).

Discussion

In this study, we for the first time examined if home-BP based guidance is effective to reduce the risk of microalbuminuria as compared with non-guidance in public school workers. Thirty-three of the 48 guided subjects (68.8%) recovered to normoalbuminuria while 30 out of 43 non-guided individuals (69.8%) recovered to normoalbuminuria one year later. Risk reduction of microalbuminuria was nearly 70% for both groups. Moreover, the amount of the reduction in UAE also was similar between the groups. These data suggest that there was no additional effect to reduce the risk of microalbuminuria in home-BP based guidance as compared with non-guidance.

How can these results be understood? In this study, guidance was given only once in the health check-up setting with no further follow-up. Thereafter, to clarify the situation on home BP measurements, a questionnaire on home BP measurements was requested one year later by the guided group. Among the 48 participants, 36 (75.0%) responded to the questionnaire. Sixteen individuals answered that they measured their BP at home routinely, whereas the remaining 20 answered that they did not measure their BP at home routinely. Nineteen participants agreed to measure their home BP using the same protocol. Both morning (134.8±12.3/89.8±9.3 mmHg vs. 131.8±13.9/86.0±11.0 mmHg, p=0.234 for systolic and p=0.132 for diastolic) and evening BP (132.9±14.6/85.6±10.9 vs. 129.1±11.4/80.9±10.3 mmHg, p=0.244 for systolic and p=0.097 for diastolic) tended to be lowered one year later as compared with the baseline, but none of the change reached a statistically significant level. Thus, the BP might not be significantly lowered at home one year later in the guided group despite significant office SBP reduction. This might limit the antialbuminuric effects of home BP-based guidance. Moreover, HbA1c and salt intake significantly increased one year after the previous year in this group. These changes might diminish the antialbuminuric effects of office BP reduction. To achieve better outcomes, long-term intervention to normalize home BP measurement may be required. Further studies are needed to clarify this issue.

We observed nearly 70% normalisation rate of microalbuminuria despite home BP-based guidance. The normalisation rate was unexpectedly high. In non-diabetic general population of the Prevention of Renal and Vascular End Stage Disease (PREVEND) study, 33.2% of 812 individuals with microalbuminuria recovered to normal or high-normal albuminuria during 4.2 years observation (17). In Takahata study, 37% of 239 non-diabetic individuals with microalbuminuria recovered to normal or high-normal albuminuria during one year observation (27). In 186 type 2 diabetic patients of African-American, 60 demonstrated microalbuminuria at baseline. During 3 years follow-up, 10 progressed to macroalbuminuria, while none recovered to normoalbuminuria (18). Therefore, the normalisation rate of microalbuminuria in our study was higher than that reported previously.

To clarify how much of the normalisation was attributed to regression to the mean effect, we examined the change in UAE from 2019 to 2020 in the normoalbuminuria and microalbuminuria groups (Supplementary material 3a, b). Scatter plots are indicated with identical lines and 95 percent confidence ellipse. It is clear that the plots are shifted under the identical line in the microalbuminuria groups, while they appear to be equally distributed around the identical line in the normoalbuminuria group. Supplementary material 4 compares the changes in UAE measurements between the normoalbuminuria and microalbuminuria groups between 2019 and 2020. UAE was significantly lower in 2020 than in 2019 in the normoalbuminuria group (p＜0.001 for both absolute and log-transformed values). Change in logUAE normalised to baseline value (%ΔlogUAE) in normoalbuminuria group (-2.2%) was much smaller than that in guided (-27.6%) or non-guided groups (-30.5%), suggesting that regression to the mean effect only partly explains the reduction of UAE in the microalbuminuria groups. In other words, a large part of the UAE reduction in the microalbuminuria groups could be attributed to factors other than regression to the mean effect.

We failed to clarify the mechanisms, but the information provided to microalbuminuric workers preceding the initiation of intervention might have favourable effects in some settings. First, as shown in the Table 1, nearly 40 % of both groups had been already treated for hypertension, and nearly 30% of non-guided group and 20% of guided group were treated with diabetes, respectively. Diagnostic information on microalbuminuria to family physicians might alert elevated risk, leading to therapeutic intensification, modification, and more intense lifestyle education (5,6). This would be especially true for hypertensive patients because the measurement of urinary albumin excretion is not allowed for hypertension treatment in the public medical insurance system in Japan (25). Thus, a new prescription for hypertension increased in both groups, and that of diabetes increased in the non-guided group as shown in the Fig. 4. Moreover, both groups demonstrated better lifestyle scores in 2020 than those in 2019. These pharmacological actions and behavioural changes may have contributed to the UAE reduction. Second, it is possible that treated patients with hypertension might have already used home BP measurements for routine care because home BP measurements have priority over clinic measurements in the diagnosis and treatment decision in Japanese hypertension guideline (25). Those workers might have benefited from home BP measurements by diagnostic provision of microalbuminuria without further guidance. Finally, sensitivity analysis excluding treated subjects has shown that results were essentially similar, suggesting that favourable effects occurred even in untreated individuals. Because contents of home-BP based guidance had been explained to all participants, individuals with high health literacy might take essential action voluntarily (28,29).

Next important issue for discussion is what factors were involved in the reduction of UAE. In the guided group, systolic BP decreased significantly one year later compared with baseline, suggesting that a reduction in the systolic BP might be primarily responsible for the reduction of UAE. It is difficult to explain this mechanism in the non-guided group. Because eGFR was significantly decreased without significant changes in BP one year later, medications that decrease glomerular pressure, such as RAS or SGLT2 inhibitors might be promoted (30). Further studies are needed to confirm this hypothesis.

Previous reports partly support our findings. It has been shown that regression of UAE was associated with decreased BP, glucose concentration, and initiation of antihypertensive drugs, such as RAS inhibitors in the PREVEND study (17), and was associated with changes in diastolic BP, eGFR, HDL cholesterol and 24-hour estimated urinary sodium excretion in Takahata study (27). It has been reported in diabetic and non-diabetic populations, a decrease in UAE, either drug induced or spontaneous, is associated with a lowered risk for cardiovascular events (31-33). So, the reduction in UAE is significant whatever the mechanism is involved.

There are several limitations to this study. First, the follow-up rate of guided and non-guided groups was 53 and 55%, respectively. A little bit high drop-out rate was due to several reasons including 1) exclusion of female subjects being menstruation either at baseline or follow-up, 2) failure to follow-up for individuals moved to different health check-up system in the next year, and 3) retirement. However, baseline data, except lifestyle score and drop-out rate were similar between guided and non-guided groups, allowing to minimize the statistical bias. Second, we failed to collect information on the qualitative changes in medicine related to UAE. So the effects of medication changes on anti-albuminuric effects, which are critically important as discussed above, still need investigation. Third, COVID-19 pandemic occurred in the early 2020. So many of the individuals who participated in the study might have suffered from unusual psycho-social stress in the latter half of the follow-up period, which may have some impact on the results. Forth, this study examined only school staffs, who are generally highly educated and with high health literacy. So it needs further study to clarify if the results are true also in other working populations with low-to-middle educational levels. Finally, UAE has been known as a very variable measure, but we examined it only once in annual health check-ups using spot urine samples. However, in a previous study, UAE measured using spot urine sample correlated with measurements from 24-hour collections, and national guidelines recommend its use in daily clinical practice (34-36). Moreover, a single measurement of the UAE has been shown to predict cardiovascular events and death in the general Japanese population (16), suggesting that this measure is a reliable biomarker.

In conclusion, temporary home BP-based guidance did not add anti-albuminuric effects to non-guidance in school workers with micro-albuminuria. Interventions to normalize home BP in a long term may be required to achieve better outcomes. Surprisingly, we observed 70% risk reduction in microalbuminuria despite the home BP-based guidance. The reason for this is unclear, but diagnostic provision for microalbuminuria and associated information might have a favourable impact on medical settings and/or individual behaviour. To clarify the mechanisms and if the case is also true in other working populations, we need further study.

The authors state that they have no Conflict of Interest (COI).

Financial Support

This study was supported by the grants and aid from the Japan Organization of Occupational Health and Safety.

Supplementary Materials

Table S1

Basline data excluding subject treated with hypertension or diabetes at baseline

Figure S1.

Individual changes in urinary albumin excretion excluding subject treated with hypertension or diabetes at baseline

Figure S2

Relationship of urinary albumin excretion (UAE) measurements between 2019 and 2020 in normoalbuminuria and microalbuminuria groups

Table S2

Comparison of the changes in UAE from 2019 to 2022 between normoalbuminuria and microalbuminuria groups