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Introduction: Breakfast-skipping habits are associated with adverse health outcomes including coronary heart disease, metabolic syndrome, and diabetes mellitus. However, it remains uncertain whether skipping breakfast affects chronic kidney disease (CKD) risk. This study aimed to examine the association between skipping breakfast and progression of CKD. Methods: We retrospectively conducted a population-based cohort study using the data from the Iki City Epidemiological Study of Atherosclerosis and Chronic Kidney Disease (ISSA-CKD). Between 2008 and 2019, we included 922 participants aged 30 years or older who had CKD (estimated glomerular filtration rate <60 mL/min/1.73 m2 and/or proteinuria) at baseline. Breakfast skippers were defined as participants who skipped breakfast more than 3 times per week. The outcome was CKD progression defined as a decline of at least 30% in the estimated glomerular filtration rate (eGFR) from the baseline status. Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for CKD progression, adjusted for other CKD risk factors. Results: During a follow-up period with a mean of 5.5 years, CKD progression occurred in 60 (6.5%) participants. The incidence rate (per 1,000 person-years) of CKD progression was 21.5 in the breakfast-skipping group and 10.7 in the breakfast-eating group (p = 0.029), respectively. The multivariable-adjusted HR (95% CI) for CKD progression was 2.60 (95% CI: 1.29–5.26) for the breakfast-skipping group (p = 0.028) compared with the group eating breakfast. There were no clear differences in the association of skipping breakfast with CKD progression in subgroup analyses by sex, age, obesity, hypertension, diabetes mellitus, baseline eGFR, and baseline proteinuria. Conclusion: Skipping breakfast was significantly associated with higher risk of CKD progression in the general Japanese population.

Chronic kidney disease (CKD) is characterized by the irreversible alteration of the function and structure of the kidney over months or years [1]. CKD contributes to increased risk of all-cause and cardiovascular mortality as well as progression to end-stage kidney disease [2, 3]. The 2017 Global Burden of Disease Study reported that the number of individuals with all-stage CKD reached almost 700 million, and 1.2 million people died from CKD in 2017 [4]. Preventive and therapeutic strategies based on up-to-date knowledge of CKD risk factors are essential to reduce the global burden of CKD.

Many prior studies have investigated the association of dietary factors with the risk of CKD. For example, a recent meta-analysis reported that higher intake of potassium and vegetables and lower intake of sodium were significantly associated with lower risk of CKD [5]. By contrast, eating habits like skipping breakfast have been found to result in adverse health outcomes including coronary heart disease [6], metabolic syndrome [7] and diabetes mellitus [8]. However, few studies have investigated whether skipping breakfast influences CKD [9‒14]. Therefore, this study aimed to examine the association between skipping breakfast and progression of CKD in a general Japanese population with CKD.

Study Design and Participants

The Iki City Epidemiological Study of Atherosclerosis and Chronic Kidney Disease (ISSA-CKD) is a population-based, retrospective cohort study of the inhabitants of Iki City, Nagasaki Prefecture, Japan. The ISSA-CKD was planned in 2017 and is still ongoing. The details of the ISSA-CKD are described elsewhere [15‒19]. Iki City is an island located in the Tsushima Strait in the north of Nagasaki Prefecture. As of March 31, 2019, the population of Iki City was 26,536, and the proportion of people aged 65 years or older was approximately 36.7%.

Iki City has held the annual health checkups every year from June to February of the following year, and adults aged 30 years or older residing in Iki City can undergo the health checkups 1 time per year. This study included participants who underwent the health checkups between 2008 and 2019 (number of visits: 1–12). We used data from the first health check as the baseline assessment (range: 2008–2019). Participants were excluded from analysis if their urine protein and estimated glomerular filtration rate (eGFR) were not measured, they were not followed up for over 1 year, they did not have CKD (eGFR <60 mL/min/1.73 m2 and/or proteinuria) at baseline, or their information on breakfast intake at baseline was missing. The Fukuoka University Medical Ethics Review Board approved the study protocol (No. 2017M010). Informed consent was obtained with an opt-out approach.

Data Collection

At baseline, we assessed breakfast-skipping habits using a self-administered questionnaire with the following question: “Do you skip breakfast more than 3 times per week?” The participants were classified into two categories (breakfast-eating or breakfast-skipping) according to their response. Information on current smoking, daily alcohol drinking, regular exercise habits, and medication use was obtained by a self-reported questionnaire. Current smokers were defined as those who had regularly smoked a total of 100 or more cigarettes for 6 months or more at baseline. Drinking habits were classified into two categories: current daily drinking and no daily drinking. Regular exercise was defined as exercising ≥30 min/day at least twice a week.

At each health checkup, fasting or non-fasting blood and random spot urine samples were collected. Serum creatinine concentration was determined enzymatically, and eGFR was determined using the formula of the Japanese Society of Nephrology as follows: eGFR (mL/min/1.73 m2) = 194 × serum creatinine (mg/dL) − 1.094 × age − 0.287 (× 0.739 if female sex) [20]. eGFR categories were then defined as stages G1‒2: eGFR ≥60; stage G3a: 45–59; stage G3b: 30–44; stage G4:15‒29; and stage G5: <15 mL/min/1.73 m2. Proteinuria was tested using the dipstick method and divided into three categories as follows: stage A1 (− or ±), stage A2 (1+), and stage A3 (≥2+). CKD was defined as an eGFR of <60 mL/min/1.73 m2 (stage G3a, G3b, G4, or G5) and/or proteinuria (stage A2 or A3).

Plasma glucose concentrations were determined using an enzymatic method, and glycated hemoglobin (HbA1c) values (National Glycohemoglobin Standardization Program value) were measured using high-performance liquid chromatography. The presence of diabetes was defined as fasting glucose concentration ≥6.99 mmol/L, non-fasting glucose concentration ≥11.1 mmol/L, HbA1c levels ≥6.5%, or the use of glucose-lowering therapies [21]. Serum low-density lipoprotein (LDL) cholesterol, high-density lipoprotein cholesterol, and triglyceride (TG) concentrations were measured enzymatically. Dyslipidemia was defined as LDL cholesterol concentration ≥3.62 mmol/L, High-density lipoprotein cholesterol concentration <1.03 mmol/L, fasting TG concentration ≥1.69 mmol/L, non-fasting TG concentration ≥1.98 mmol/L, or the use of lipid-lowering therapies. Height and weight were measured with light clothing and without shoes. The body mass index (BMI) was calculated as the body weight (kg) divided by the square of the height (m). Obesity was defined as a BMI of ≥25 kg/m2 [22]. Blood pressure (BP) was measured by trained staff using mercury, automated, or aneroid sphygmomanometers in the right upper arm with the appropriate cuff size, after at least 5 min of rest in the sitting position. BP was measured twice, and the mean of the two measurements was used in this study. Hypertension was defined as a systolic BP of ≥140 mm Hg, a diastolic BP of ≥90 mm Hg, or the use of BP-lowering medication [23]. All these parameters were performed at each health checkup.

Participant Follow-Up and Definition of Outcomes

The follow-up period for each participant spanned from the first annual health check visit to the last visit, between 2008 and 2019. The outcome of the present study was progression of CKD. CKD progression was defined as the first event that was a decline of at least 30% in eGFR from baseline status. We furthermore confirmed that >30% decrease in GFR persisted to the last visit by the final examination value.

Statistical Analysis

The baseline characteristics of the study population were compared according to two groups of breakfast intake. Continuous variables were expressed as the mean (standard deviation), and categorical variables were expressed as the number of participants (percentages). Differences between the two groups were evaluated using analysis of variance for continuous variables and the χ2 test for categorical variables. Incidence rates of CKD progression were calculated using the person-year approach by each of the two groups of breakfast intake. We used the Cox proportional hazards models to estimate crude and multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (95% CIs). The multivariable-adjusted model included sex, age, current smoking status, daily alcohol intake, regular exercise, BMI, systolic BP, HbA1c, LDL cholesterol, baseline eGFR, and urinary protein categories (stages A1, A2, and A3). Subgroup analyses of the association between breakfast intake and CKD progression were conducted for the following variables: sex, age (<65 years compared with ≥65 years), obesity, hypertension, diabetes mellitus, baseline eGFR (≥60 mL/min/1.73 m2 compared with <60 mL/min/1.73 m2), and baseline proteinuria (stage A1 compared with stages A2 and A3). Subgroup differences were tested by adding interaction terms to the statistical models. We considered a two-sided p value of <0.05 to be statistically significant. All data analyses were performed using SAS version 9.4 for Windows (SAS Institute Inc., Cary, NC, USA).

Study Population

A flowchart depicting study population selection is shown in Figure 1. Between 2008 and 2019, 8,268 people aged 30 years or older underwent annual health checks at the time of enrollment. We excluded participants who had missing data on urine protein and eGFR (n = 386), breakfast intake (n = 496), and CKD (n = 4,599) at baseline, and those who did not attend a follow-up examination (n = 1,865). The final study analysis included 922 participants who had CKD at baseline.

Fig. 1.

Flowchart of selecting the study population. CKD is defined as eGFR <60 mL/min/1.73 m2 and/or proteinuria. eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease.

Fig. 1.

Flowchart of selecting the study population. CKD is defined as eGFR <60 mL/min/1.73 m2 and/or proteinuria. eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease.

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Baseline Characteristics

The mean age of study participants at baseline was 64.2 years. The mean eGFR at baseline was 58.2 mL/min/1.73 m2. Of 922 study participants, 478 (51.8%) were male. The breakfast-eating group included 820 participants (88.9%), and the breakfast-skipping group included 102 (11.1%) participants. Of all respondents, 852 (92.4%) had CKD stages G1‒2 to G3a. Table 1 shows the baseline characteristics of the study population by breakfast intake group. Participants in the breakfast-skipping group were younger, more likely to be current smokers and daily drinkers, less likely to exercise regularly, and had higher BMI and eGFR levels and more frequent proteinuria than those in the breakfast-eating group (all p < 0.05).

Table 1.

Baseline characteristics of participants by breakfast intake group

VariablesAll (N = 922)Breakfastap value
eating (N = 820)skipping (N = 102)
Men, N (%) 478 (51.8) 417 (50.9) 61 (59.8) 0.088 
Age, mean (SD), years 64.2 (7.3) 64.8 (6.4) 59.8 (11.1) <0.001 
Current smokers, N (%) 138 (15.0) 108 (13.2) 30 (29.4) <0.001 
Daily drinking, N (%) 196 (21.4) 159 (19.5) 37 (36.6) <0.001 
Regular exerciseb, N (%) 302 (33.0) 285 (35.0) 17 (16.8) <0.001 
Obesityc, N (%) 372 (40.4) 322 (39.3) 50 (49.0) 0.058 
BMI, mean (SD), kg/m2 24.3 (3.6) 24.2 (3.6) 25.1 (4.0) 0.023 
Hypertensiond, N (%) 552 (59.9) 493 (60.1) 59 (57.8) 0.658 
Systolic blood pressure, mean (SD), mm Hg 132.9 (17.9) 132.8 (17.5) 133.8 (20.8) 0.576 
Diastolic blood pressure, mean (SD), mm Hg 76.0 (11.1) 75.8 (10.7) 77.2 (13.8) 0.252 
Diabetes mellituse, N (%) 140 (15.2) 123 (15.0) 17 (16.7) 0.658 
HbA1c, mean (SD), % 5.4 (0.9) 5.4 (0.9) 5.4 (1.1) 0.789 
Dyslipidemiaf, N (%) 551 (59.8) 493 (60.1) 58 (56.9) 0.527 
HDL cholesterol, mean (SD), mmol/L 1.53 (0.43) 1.52 (0.43) 1.59 (0.47) 0.163 
LDL cholesterol, mean (SD), mmol/L 3.18 (0.84) 3.17 (0.85) 3.31 (0.79) 0.118 
Triglyceride, mean (SD), mmol/L 1.49 (0.97) 1.48 (0.95) 1.56 (1.08) 0.450 
eGFR, mean (SD), mL/min/1.73 m2 58.2 (14.1) 57.4 (13.1) 64.2 (19.6) <0.001 
eGFR stagesg    <0.001 
 G1‒2, N (%) 210 (22.8) 169 (20.6) 41 (40.2)  
 G3a, N (%) 642 (69.6) 591 (72.1) 51 (50.0)  
 G3b, N (%) 57 (6.2) 49 (6.0) 8 (7.8)  
 G4, N (%) 8 (0.9) 7 (0.9) 1 (1.0)  
 G5, N (%) 5 (0.5) 4 (0.5) 1 (1.0)  
Urinary protein stagesh    <0.001 
 A1, N (%) 619 (67.1) 568 (69.2) 51 (50.0)  
 A2, N (%) 263 (28.5) 214 (26.1) 49 (48.0)  
 A3, N (%) 40 (4.3) 38 (4.6) 2 (2.0)  
VariablesAll (N = 922)Breakfastap value
eating (N = 820)skipping (N = 102)
Men, N (%) 478 (51.8) 417 (50.9) 61 (59.8) 0.088 
Age, mean (SD), years 64.2 (7.3) 64.8 (6.4) 59.8 (11.1) <0.001 
Current smokers, N (%) 138 (15.0) 108 (13.2) 30 (29.4) <0.001 
Daily drinking, N (%) 196 (21.4) 159 (19.5) 37 (36.6) <0.001 
Regular exerciseb, N (%) 302 (33.0) 285 (35.0) 17 (16.8) <0.001 
Obesityc, N (%) 372 (40.4) 322 (39.3) 50 (49.0) 0.058 
BMI, mean (SD), kg/m2 24.3 (3.6) 24.2 (3.6) 25.1 (4.0) 0.023 
Hypertensiond, N (%) 552 (59.9) 493 (60.1) 59 (57.8) 0.658 
Systolic blood pressure, mean (SD), mm Hg 132.9 (17.9) 132.8 (17.5) 133.8 (20.8) 0.576 
Diastolic blood pressure, mean (SD), mm Hg 76.0 (11.1) 75.8 (10.7) 77.2 (13.8) 0.252 
Diabetes mellituse, N (%) 140 (15.2) 123 (15.0) 17 (16.7) 0.658 
HbA1c, mean (SD), % 5.4 (0.9) 5.4 (0.9) 5.4 (1.1) 0.789 
Dyslipidemiaf, N (%) 551 (59.8) 493 (60.1) 58 (56.9) 0.527 
HDL cholesterol, mean (SD), mmol/L 1.53 (0.43) 1.52 (0.43) 1.59 (0.47) 0.163 
LDL cholesterol, mean (SD), mmol/L 3.18 (0.84) 3.17 (0.85) 3.31 (0.79) 0.118 
Triglyceride, mean (SD), mmol/L 1.49 (0.97) 1.48 (0.95) 1.56 (1.08) 0.450 
eGFR, mean (SD), mL/min/1.73 m2 58.2 (14.1) 57.4 (13.1) 64.2 (19.6) <0.001 
eGFR stagesg    <0.001 
 G1‒2, N (%) 210 (22.8) 169 (20.6) 41 (40.2)  
 G3a, N (%) 642 (69.6) 591 (72.1) 51 (50.0)  
 G3b, N (%) 57 (6.2) 49 (6.0) 8 (7.8)  
 G4, N (%) 8 (0.9) 7 (0.9) 1 (1.0)  
 G5, N (%) 5 (0.5) 4 (0.5) 1 (1.0)  
Urinary protein stagesh    <0.001 
 A1, N (%) 619 (67.1) 568 (69.2) 51 (50.0)  
 A2, N (%) 263 (28.5) 214 (26.1) 49 (48.0)  
 A3, N (%) 40 (4.3) 38 (4.6) 2 (2.0)  

SD, standard deviation; eGFR, estimated glomerular filtration rate.

aBreakfast-eating group: participants who ate breakfast more than 5 times per week; breakfast-skipping group: participants who skipped breakfast more than 3 times per week.

bExercise habits of ≥30 min each day for twice or more each week.

cBody mass index of ≥25 kg/m2.

dBlood pressure of ≥140/90 mm Hg or the use of blood pressure-lowering therapies.

eFasting glucose concentration ≥6.99 mmol/L, non-fasting glucose concentration ≥11.1 mmol/L, HbA1c levels ≥6.5%, or the use of glucose-lowering therapies.

fLDL cholesterol concentration ≥3.62 mmol/L, HDL cholesterol concentration <1.03 mmol/L, fasting triglyceride concentration ≥1.69 mmol/L, non-fasting triglyceride concentration ≥1.98 mmol/L, or use of lipid-lowering therapies.

gStage G1‒2: eGFR ≥60; stage G3a: 45–59; stage G3b: 30–44; stage G4:15‒29; and stage G5: <15 mL/min/1.73 m2.

hStage A1 (− or ±), stage A2 (1+), and stage A3 (≥2+).

Skipping Breakfast and Risk of CKD Progression

During a mean follow-up period of 5.5 years, CKD progression occurred in 60 (6.5%) participants. Table 2 shows the risks of CKD progression according to breakfast intake group. The incidence rate of CKD progression was approximately two-fold higher in the breakfast-skipping group (21.5 per 1,000 person-years) compared with the breakfast-eating group (10.7 per 1,000 person-years) (p = 0.029). Skipping breakfast was significantly associated with higher risk of CKD progression even after including confounding factors as model covariates: the multivariable-adjusted HR (95% CI) for the progression of CKD was 2.60 (95% CI: 1.29–5.26) in the breakfast-skipping group compared with the breakfast-eating group (p = 0.008).

Table 2.

Risk of CKD progression by breakfast intake group

CKD progressionaBreakfastbp value
eating (N = 820)skipping (N = 102)
Cases, N (%) 49 (6.0) 11 (10.8)  
Person-years 4,560 511  
Incidence rate (per 1,000 person-years) 10.7 21.5  
Crude HR (95% CI) 1.00 (reference) 2.08 (1.08–4.00) 0.029 
Adjusted HR (95% CI) 1.00 (reference) 2.60 (1.29–5.26) 0.008 
CKD progressionaBreakfastbp value
eating (N = 820)skipping (N = 102)
Cases, N (%) 49 (6.0) 11 (10.8)  
Person-years 4,560 511  
Incidence rate (per 1,000 person-years) 10.7 21.5  
Crude HR (95% CI) 1.00 (reference) 2.08 (1.08–4.00) 0.029 
Adjusted HR (95% CI) 1.00 (reference) 2.60 (1.29–5.26) 0.008 

CKD, chronic kidney disease; CI, confidence interval; eGFR, estimated glomerular filtration rate.

Adjusted for sex, age, current smoking status, daily alcohol intake, regular exercise, body mass index, systolic blood pressure, HbA1c, LDL cholesterol, baseline eGFR, and urinary protein categories (stages A1, A2, and A3).

aA decline of at least 30% in eGFR from the baseline status.

bBreakfast-eating group: participants who ate breakfast more than 5 times per week; breakfast-skipping group: participants who skipped breakfast more than 3 times per week.

Multivariable-Adjusted HRs for CKD Progression according to Subgroups

Table 3 shows multivariable-adjusted HRs (95% CIs) of breakfast intake for CKD progression according to subgroups. Subgroup analyses of the associations between breakfast intake and CKD progression by sex, age (<65 years vs. ≥65 years), obesity, hypertension, diabetes mellitus, baseline eGFR (≥60 mL/min/1.73 m2 vs. <60 mL/min/1.73 m2), and baseline proteinuria (stage A1 vs. stages A2 and A3) showed no clear between-group differences (all p > 0.3 for interaction).

Table 3.

Multivariable-adjusted HRs of breakfast intake for CKD progression according to subgroups

SubgroupsBreakfastap value for interaction
eating (N = 820)skipping (N = 102)
Male (N = 478) 1 (reference) 2.26 (0.80–6.38) 0.989 
Female (N = 444) 1 (reference) 3.29 (1.15–9.42)  
Age <65 years (N = 414) 1 (reference) 2.18 (0.74–6.42) 0.573 
Age ≥65 years (N = 508) 1 (reference) 4.34 (1.58–12.0)  
Obesity (+) (N = 372) 1 (reference) 2.03 (0.74–5.54) 0.381 
Obesity (−) (N = 550) 1 (reference) 3.17 (1.12–9.01)  
Hypertension (+) (N = 552) 1 (reference) 2.77 (1.21–6.34) 0.677 
Hypertension (−) (N = 370) 1 (reference) 5.21 (1.22–22.2)  
Diabetes mellitus (+) (N = 140) 1 (reference) 2.13 (0.68–6.66) 0.434 
Diabetes mellitus (−) (N = 782) 1 (reference) 2.88 (1.11–7.42)  
Baseline eGFR ≥60 mL/min/1.73 m2 (N = 210) 1 (reference) 2.05 (0.62–6.84) 0.581 
Baseline eGFR <60 mL/min/1.73 m2 (N = 712) 1 (reference) 2.75 (1.04–7.32)  
Baseline proteinuria (+) (N = 303) 1 (reference) 2.53 (1.06–6.09) 0.741 
Baseline proteinuria (−) (N = 619) 1 (reference) 2.13 (0.58–7.84)  
SubgroupsBreakfastap value for interaction
eating (N = 820)skipping (N = 102)
Male (N = 478) 1 (reference) 2.26 (0.80–6.38) 0.989 
Female (N = 444) 1 (reference) 3.29 (1.15–9.42)  
Age <65 years (N = 414) 1 (reference) 2.18 (0.74–6.42) 0.573 
Age ≥65 years (N = 508) 1 (reference) 4.34 (1.58–12.0)  
Obesity (+) (N = 372) 1 (reference) 2.03 (0.74–5.54) 0.381 
Obesity (−) (N = 550) 1 (reference) 3.17 (1.12–9.01)  
Hypertension (+) (N = 552) 1 (reference) 2.77 (1.21–6.34) 0.677 
Hypertension (−) (N = 370) 1 (reference) 5.21 (1.22–22.2)  
Diabetes mellitus (+) (N = 140) 1 (reference) 2.13 (0.68–6.66) 0.434 
Diabetes mellitus (−) (N = 782) 1 (reference) 2.88 (1.11–7.42)  
Baseline eGFR ≥60 mL/min/1.73 m2 (N = 210) 1 (reference) 2.05 (0.62–6.84) 0.581 
Baseline eGFR <60 mL/min/1.73 m2 (N = 712) 1 (reference) 2.75 (1.04–7.32)  
Baseline proteinuria (+) (N = 303) 1 (reference) 2.53 (1.06–6.09) 0.741 
Baseline proteinuria (−) (N = 619) 1 (reference) 2.13 (0.58–7.84)  

CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate.

Values are hazard ratios (95% confidence intervals) adjusted for sex (except for subgroup analysis by sex), age (except for subgroup analysis by age), current smoking status, daily alcohol intake, regular exercise, body mass index (except for subgroup analysis by obesity), systolic blood pressure (except for subgroup analysis by hypertension), HbA1c (except for subgroup analysis by diabetes mellitus), LDL cholesterol, baseline eGFR (except for subgroup analysis by baseline eGFR), and urinary protein categories (except for subgroup analysis by baseline proteinuria). Obesity: body mass index ≥25 kg/m2. Hypertension: blood pressure of ≥140/90 mm Hg or the use of blood pressure-lowering therapy. Diabetes mellitus: fasting glucose concentration ≥6.99 mmol/L, non-fasting glucose concentration ≥11.1 mmol/L, HbA1c levels ≥6.5%, or the use of glucose-lowering therapies.

aBreakfast-eating group: participants who ate breakfast more than 5 times per week; breakfast-skipping group: participants who skipped breakfast more than 3 times per week.

In this observational study, over a mean follow-up period of 5.5 years, we examined the association between breakfast-skipping habits and progression of CKD in a general Japanese population with CKD, predominantly at stages G1 to G3a CKD (eGFR of ≥45 mL/min/1.73 m2). Individuals who skipped breakfast more than 3 times per week had a higher risk of CKD progression, defined as a decline of 30% or more in eGFR, compared with those who ate breakfast ≥5 times per week. This risk remained significant even when adjusted for sex, age, current smoking status, daily alcohol intake, regular exercise, BMI, systolic BP, HbA1c, LDL cholesterol, baseline eGFR, and urinary protein categories (stages A1, A2, and A3). We did not detect clear differences in the associations of skipping breakfast with CKD progression between subgroups defined by sex, age, obesity, hypertension, diabetes mellitus, baseline eGFR, or baseline proteinuria stage.

A few prior studies have reported an association between skipping breakfast and risk of CKD. In a population-based cross-sectional study of 4,370 Koreans, participants who rarely had breakfast showed a higher risk of new-onset CKD than those who had breakfast 5–7 times per week [13]. A retrospective cohort study using an employment-based health insurance claims database linked with specific health checkups examined the association between lifestyle behaviors and initiation of renal replacement therapy in 60,481 CKD patients and found that frequent breakfast-skipping was significantly associated with dialysis initiation over a median follow-up period of 2.3 years [12]. In the Japanese population, there are three retrospective studies that investigated the association between skipping breakfast and the incidence of CKD [10, 11] or proteinuria [14]. However, there was no study that has investigated skipping breakfast in the Japanese population and its association with the CKD progression. To our knowledge, this is the first study to investigate the effects of breakfast-skipping habits on eGFR decline in a general Japanese population with CKD. Our study confirms findings from previous studies, showing a significant association between skipping breakfast and CKD progression.

Our study found the harmful effect of skipping breakfast on CKD. The harmful effect associated with skipping breakfast might be attributable to type of fasting. During Ramadan, Muslims must abstain from eating and drinking from dawn to sunset every day for a whole month. In a prospective observational study of 65 adults with stage 3 or higher CKD, Ramadan fasting was associated with worsening of kidney function [24]. This result is consistent with our finding. Whereas intermittent fasting has been suggested to play a renoprotective role in the progression of diabetic nephropathy through improving lipid metabolism, controlling BP, maintaining mitochondrial homeostasis, increasing the production of ketone bodies, and promoting autophagy [25]. Further studies are needed to determine the effect of fasting on kidney disease.

Possible mechanisms by which skipping breakfast causes progression of CKD remain unclear. (1) Some studies have reported that omitting breakfast causes insulin resistance [26, 27]. Insulin resistance has been shown to promote histological changes of the kidney in mice including podocyte actin disorganization and loss of foot process structure, increasing extracellular matrix, thickening of glomerular basement membrane, podocyte apoptosis, and glomerulosclerosis using podocyte-specific insulin receptor knockout [28]. Therefore, insulin resistance caused by skipping breakfast may contribute to progression of CKD. (2) Other reports found that skipping breakfast was associated with increased arterial stiffness [29, 30]. Increased arterial stiffness contributes to excessive delivery of pulsatile energy to the kidneys, leading to microvascular damage and loss of function [31]. Therefore, CKD patients who skip breakfast might have higher risk of eGFR decline via increased arterial stiffness. (3) A retrospective study suggested that skipping breakfast was significantly associated with nonadherence to medications in patients with diabetes [32]. Poor adherence to medications including kidney-protecting properties (e.g., angiotensin-converting enzyme inhibitors, angiotensin-Ⅱ receptor antagonists, sodium-glucose cotransporter 2 inhibitors, and mineralocorticoid receptor antagonists) associated with breakfast-skipping might also contribute to the link between breakfast-skipping and CKD progression.

There are some limitations in this study. First, because of the retrospective nature of the study design, we could not completely exclude the possibility of residual confounding due to unmeasured factors. For example, we do not have information on cause for breakfast skipping. Lifestyle factor such as sleep disturbances, time of sleeping, shift work and working hours, stress, socioeconomic condition, and family structure could further confound our results. Second, our findings may have been influenced by selection bias. For example, people who were concerned about their health were more likely to undergo the health checkups and to be included in the present analysis than those who were not concerned about their health. Third, we do not have information on diet content or daily energy intake in the present analysis. Forth, because the study population had CKD predominantly at stages G1 to G3a CKD, caution is warranted when applying our findings to populations with CKD at stages G3b to G5. Finally, in this study, the number of outcome events (progression of CKD: 11 events) and the number of participants (n = 102) in the breakfast-skipping group are somewhat small. Further research with large sample sizes is warranted to verify our findings.

In conclusion, skipping breakfast was associated with increased risk of CKD progression in individuals with CKD (dominantly mild to moderate eGFR stages). Interventions targeted at changing unhealthy eating habits such as skipping breakfast may help prevent CKD progression.

We thank Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

This study was conducted in accordance with the Declaration of Helsinki. The study protocol was reviewed and approved by the Fukuoka University Medical Ethics Review Board, approval No. 2017M010. Written informed consent was not required because consent from all participants was obtained using the opt-out approach, and this was approved by the abovementioned committee.

The authors have no conflicts of interest to declare.

This study was not supported by any sponsor or funder.

K.T. (Koji Takahashi), T.M., H.A., and K.M. designed the study. K.T. (Koji Takahashi), Y.I., K.T. (Kazuhiro Tada), and T.Y. collected the study data. K.T. (Koji Takahashi) and H.A. performed the data analyses. K.T. (Koji Takahashi) wrote the first draft of the manuscript. H.A. and K.M. revised the manuscript. H.H., K.I., T.S., S.K, Y.S., C.N., S.O., M.A., A.S., M.K., T.M., C.Y., and S.M. contributed to the discussion and reviewed the manuscript. All authors approved the final version.

Data of the present study were provided by the Iki Health Center, Nagasaki, Japan, and the authors do not have the right to share them. In order to gain access to the data, contact Iki Health Center (https://www.city.iki.nagasaki.jp). Further inquiries can be directed to the corresponding author.

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