Impact of lifestyle behaviors on the development of lifestyle diseases: A retrospective cohort study

Takafumi Okawa; Hikaru Negishi; Yuki Aoki; Mitsuo Uchida; Yumi Sato; Mai Ishikawa; Rie Matsui; Kaori Hotta; Takayuki Saitoh

doi:10.1371/journal.pone.0327505

. 2025 Jul 29;20(7):e0327505. doi: 10.1371/journal.pone.0327505

Impact of lifestyle behaviors on the development of lifestyle diseases: A retrospective cohort study

Takafumi Okawa ^1,^2,^#, Hikaru Negishi ^2,^#, Yuki Aoki ^1,^#, Mitsuo Uchida ^1,^#, Yumi Sato ^3,^#, Mai Ishikawa ³, Rie Matsui ³, Kaori Hotta ³, Takayuki Saitoh ^2,^*

Editor: Mulu Tiruneh⁴

PMCID: PMC12306743 PMID: 40729338

Abstract

Background

Questionnaires are used to collect data on lifestyle behaviors during specific health checkups; however, the results cannot conclusively determine whether the behaviors influence the onset of lifestyle diseases. By analyzing data from a retrospective cohort, this study aimed to determine the specific lifestyle behaviors that most strongly contribute to the onset of lifestyle diseases, such as metabolic syndrome, hypertension, diabetes, and dyslipidemia.

Methods

We administrated the data of 924,932 individuals insured under Gunma Prefecture’s National Health Insurance who underwent specific health checkups between 2011 and 2016. A logistic regression analysis was conducted to assess the association between the responses to 10 lifestyle questions and the future onset of lifestyle diseases.

Results

We examined 47,803 individuals who were not identified with lifestyle disorders at the initial checkup. In this study, weight gain of ≥10 kg since the age of 20 years showed the strongest association with MetS (OR: 2.01; 95% CI, 1.79–2.25). Additionally, smoking and weight gain were identified as common risk factors for MetS, hypertension, and dyslipidemia. The results revealed that lifestyle behaviors are longitudinally associated with the onset of lifestyle diseases.

Conclusion

The use of self-administered questionnaires to assess lifestyle behaviors can effectively predict future health risks.

Introduction

Aging has recently become a prevalent issue in developed countries. Japan, particularly, is one of the leading super-aged societies worldwide. The increase in non-communicable diseases (NCDs) with the aging population has led to a rise in national healthcare expenditures, making cost containment a critical public health priority [1–3]. Specific health checkups (SHC) have emerged as initiatives aimed at curbing the rising national healthcare costs in Japan’s aging society [4].

The SHC program provides health screening and promotion services tailored to insured individuals aged 40–74 years and their dependents. Its primary goal is to prevent bedridden conditions in older adults through the early detection and intervention of metabolic syndrome (MetS). The SHC is a unique initiative in Japan, established by the Ministry of Health, Labour and Welfare (MHLW) in 2008 under the “Act on Assurance of Medical Care for Elderly People” [4]. Currently, approximately 25.4 million beneficiaries are covered by the municipal national health insurance, representing a significant effort to promote the health of older adults at the municipal level [5]. Notably, the MHLW’s “Guide to Creating a Data Health Plan, 3rd Revised Edition” emphasizes that utilizing SHC data can facilitate early detection of lifestyle diseases, thereby contributing to the reduction of national medical care expenditures [6]. To date, health promotion initiatives, utilizing the SHC data, are currently being implemented in Shizuoka and Osaka Prefectures [7,8].

The SHC utilizes the use of anthropometric measurements, laboratory values, and self-administered questionnaires designed to assess lifestyle behaviors. These questionnaires conform to the “standard questionnaire” established by the MHLW [9]. However, the evidence derived from the questionnaire results was not consistent in predicting the onset of lifestyle diseases [10–14]. For example, some studies highlight a correlation between skipping breakfast and the onset of diabetes [15,16], whereas others refute this connection [17]. This inconsistency has resulted in the inadequate utilization of data for formulating health management policies.

The discrepancy in conclusions stems from several factors, such as the inclusion of individuals with positive results, which could affect the accurate assessment of the effects of lifestyle behaviors [18], inadequate long-term evaluations [17], and insufficient consideration of the interaction effects of lifestyle behaviors on the onset of lifestyle diseases. Addressing these issues through the use of adequate sample size and the conduct of a longitudinal analysis could lead to more consistent conclusions regarding this relationship.

Using SHC data collected over 10 years from the National Health Insurance (NHI) database, our retrospective cohort study aimed to investigate lifestyle behaviors that are genuinely associated with the onset of lifestyle diseases such as MetS and its components such as hypertension, diabetes, and dyslipidemia.

Materials and methods

Database

We used SHC data from the Gunma Prefecture’s NHI database, covering a 10-year period from 2011 to 2020. The Gunma Prefecture has a population of approximately 2 million, with about 20% of individuals’ insurance data updated annually in the NHI database.

Study population

The selection process for the study population is outlined in Fig 1. The study included 924,932 total counts of individuals insured under Gunma Prefecture’s NHI who underwent SHC between 2011 and 2016 (150,610 in 2011, 154,579 in 2012, 156,454 in 2013, 156,531 in 2014, 155,690 in 2015, and 151,068 in 2016). The onset of lifestyle diseases was assessed 4 years after the initial checkup, based on previous research [11]. To ensure the suitability of the study samples, individuals with missing health checkup data 4 years post-initial checkup (N = 447,511); whose presence or absence of lifestyle diseases were not determined (N = 3,470); who were identified with MetS, diabetes, hypertension, or dyslipidemia at the initial checkup (N = 363,453); who reported a history of stroke, heart disease, or chronic kidney failure in the self-administered questionnaire during the initial health checkup (N = 3,946); and who underwent multiple checkups during the study period (N = 58,749) were excluded. Hence, 47,803 participants were included in the final analysis.

Variables

The following data were extracted from the SHC records: personal information (sex, date of birth, and date of SHC); anthropometric measurements (body mass index [BMI] and waist circumference [WC]); laboratory values (systolic blood pressure [SBP], diastolic blood pressure [DBP], hemoglobin A1c [HbA1c], triglyceride [TG], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], aspartate aminotransferase [AST], alanine aminotransferase [ALT], and γ-glutamyl transpeptidase [γ-GTP] levels). The results of self-administered questionnaire survey on lifestyle behaviors (smoking status, weight change, exercise habits, eating habits, drinking habits, and sleep habits), medications used (for hypertension, dyslipidemia, and diabetes), and disease history (stroke, heart disease, and kidney disease) were also extracted. Age, BMI, and laboratory values were categorized according to the medical guidelines, as detailed in S1 Table. Data on lifestyle behaviors at the initial checkup were extracted from the results of the “standard questionnaire” provided by the MHLW. From this questionnaire, 11 questions that have consistently provided valid responses over the years were selected (as shown in S2 Table). The presence or absence of lifestyle behavior risks is also presented in S2 Table. With regard to drinking habits, the presence or absence of risk was determined based on the frequency of drinking (Question 9) and amount of alcohol consumed (Question 10), following the criteria used in previous studies [19]. Individuals who reported drinking “every day” or “sometimes” and consuming “180 mL or more” of alcohol were classified as having a risk. Conversely, those who drank “hardly ever” or consumed “less than 180 mL” were classified as not having a risk. A serving of 180 mL of refined saké (rice wine) is equivalent to a medium bottle of beer (500 mL), 110 mL of shochu (25% alcohol content), a double shot of whiskey (60 mL), or two glasses of wine (240 mL).

In this study, we selected MetS and its components—hypertension, diabetes, and dyslipidemia—to investigate their association with lifestyle behaviors. MetS was defined according to the criteria of the National Health and Nutrition Survey (NHNS) [20], which requires the presence of visceral fat accumulation (WC ≥ 85 cm in men and ≥ 90 cm in women) and two or more of the following criteria: 1) an SBP > 130 mmHg, DBP of >85 mmHg, or the use of antihypertensive drugs; 2) an HDL-C level <40 mg/dL or the use of antihyperlipidemic drugs; and 3) an HbA1c level ≥6.0% or the use of antidiabetic drugs. Hypertension was defined as an SBP ≥ 140 mmHg, a DBP ≥ 90 mmHg, or the use of antihypertensive drugs. Diabetes was defined as an HbA1c level ≥6.5% or the use of antidiabetic drugs. Dyslipidemia was defined as a TG level ≥150 mg/dL, an HDL-C level <40 mg/dL, an LDL-C level ≥140 mg/dL, or the use of antihyperlipidemic drugs. Individuals who did not meet any of the above criteria were considered healthy.

Statistical analysis

Chi-square tests were performed to compare the participant characteristics. The odds ratios (OR) and corresponding 95% confidence intervals (CI) for the onset of lifestyle diseases were calculated using a logistic regression model. Initially, a simple regression model was employed, using 10 lifestyle behaviors as independent variables and MetS, hypertension, diabetes, and dyslipidemia as dependent variables. Two adjusted models were subsequently employed. Model 1 included the 10 lifestyle behaviors as independent variables and was adjusted for sex and age as covariates. Model 2 was adjusted for Model 1 and addition of BMI and laboratory tests as covariates. Correlation coefficients and variance inflation factors (VIFs) were calculated for each variable to assess multicollinearity among the independent variables, confirming that all VIF values were below 5. All data were analyzed using Python 3.10 and a p value of <0.05 was considered significant.

Ethical considerations

The study was approved by the Ethics Committee for Research Involving Humans of the Gunma University Faculty of Medical, Gunma, Japan (HS2023−137). This study involved a secondary analysis of data from the existing Kokuho database. As the data had already been anonymized at the time of acquisition, informed consent was not required.

Results

The characteristics of participants who developed lifestyle diseases and those who did not are presented in Table 1. The BMI and laboratory values were compared between the participants who developed lifestyle diseases and those who did not (S3 Table). The prevalence of MetS 4 years after the initial health checkup was 4.8%. Individuals with MetS were more likely to engage in risky lifestyle behaviors, such as smoking, weight gain ≥10 kg since the age of 20 years, decreased physical activity, slow walking, fast eating, eating before bedtime, skipping breakfast, and problematic drinking habits. Conversely, they were less likely to exhibit adopting poor sleeping habits. The prevalence of hypertension 4 years after the initial health checkup was 26.1%. Individuals with hypertension were more likely to exhibit risky lifestyle behaviors such as smoking, weight change ≥10 kg since the age of 20 years, eating before bedtime, and problematic drinking habits. Conversely, they were less likely to exhibit risky behaviors, including the lack of regular exercise, decreased physical activity, skipping breakfast, and adopting poor sleeping habits. The prevalence of diabetes 4 years after the initial health checkup was 2.5%. Individuals with diabetes were more likely to exhibit risky lifestyle behaviors such as smoking, weight change ≥10 kg since the age of 20 years, and fast eating. Conversely, they were less likely to engage in risk behaviors, such as the lack of regular exercise, skipping breakfast, and adopting poor sleeping habits. The prevalence of dyslipidemia 4 years after the initial health checkup was 33.2%. Individuals with dyslipidemia were more likely to exhibit risky lifestyle behaviors, such as smoking, weight change ≥10 kg since the age of 20 years, the lack of regular exercise, decreased physical activity, fast eating, skipping breakfast, and adopting poor sleeping habits. Conversely, they were less likely to exhibit eating behaviors before bedtime and adopt problematic drinking habits.

Table 1. Baseline characteristics of individuals who developed and did not develop lifestyle diseases.

		Metabolic syndrome					Hypertension					Diabetes					Dyslipidemia
		Undeveloped		Developed		P value^a	Undeveloped		Developed		P value^a	Undeveloped		Developed		P value^a	Undeveloped		Developed		P value^a
		n = 45,507		n = 2,296			n = 35,339		n = 12,464			n = 46,600		n = 1,203			n = 31,930		n = 15,873
		n	(%)	n	(%)		n	(%)	n	(%)		n	(%)	n	(%)		n	(%)	n	(%)
Attributes
Sex
	Men	17,015	(37.4)	1,636	(71.3)	< 0.001	13,189	(37.3)	5,462	(43.8)	< 0.001	17,966	(38.6)	685	(56.9)	< 0.001	12,851	(40.2)	5,800	(36.5)	< 0.001
	Women	28,492	(62.6)	660	(28.7)		22,150	(62.7)	7,002	(56.2)		28,634	(61.4)	518	(43.1)		19,079	(59.8)	10,073	(63.5)
Age
	40-44	3,225	(7.1)	110	(4.8)	< 0.001	2,936	(8.3)	399	(3.2)	< 0.001	3,301	(7.1)	34	(2.8)	< 0.001	2,498	(7.8)	837	(5.3)	< 0.001
	45-49	3,203	(7.0)	126	(5.5)		2,791	(7.9)	538	(4.3)		3,288	(7.1)	41	(3.4)		2,287	(7.2)	1,042	(6.6)
	50-54	3,027	(6.7)	134	(5.8)		2,544	(7.2)	617	(5.0)		3,114	(6.7)	47	(3.9)		2,042	(6.4)	1,119	(7.0)
	55-59	3,946	(8.7)	213	(9.3)		3,184	(9.0)	975	(7.8)		4,066	(8.7)	93	(7.7)		2,529	(7.9)	1,630	(10.3)
	60-64	9,203	(20.2)	498	(21.7)		6,925	(19.6)	2,776	(22.3)		9,438	(20.3)	263	(21.9)		6,055	(19.0)	3,646	(23.0)
	65-69	16,194	(35.6)	957	(41.7)		11,768	(33.3)	5,383	(43.2)		16,589	(35.6)	562	(46.7)		11,231	(35.2)	5,920	(37.3)
	≥ 70	6,709	(14.7)	258	(11.2)		5,191	(14.7)	1,776	(14.2)		6,804	(14.6)	163	(13.5)		5,288	(16.6)	1,679	(10.6)
Current Smoking
	Yes	6,077	(13.4)	524	(22.8)	< 0.001	4,739	(13.4)	1,862	(14.9)	< 0.001	6,395	(13.7)	206	(17.1)	0.001	4,325	(13.5)	2,276	(14.3)	0.018
	No	39,427	(86.6)	1,770	(77.2)		30,598	(86.6)	10,599	(85.1)		40,201	(86.3)	996	(82.9)		27,602	(86.5)	13,595	(85.7)
Weight gain of ≥10 kg since age 20
	Yes	7,087	(17.3)	1,162	(57.2)	< 0.001	5,590	(17.6)	2,659	(23.5)	< 0.001	7,929	(18.9)	320	(29.4)	< 0.001	5,083	(17.7)	3,166	(22.0)	< 0.001
	No	33,984	(82.7)	870	(42.8)		26,218	(82.4)	8,636	(76.5)		34,087	(81.1)	767	(70.6)		23,635	(82.3)	11,219	(78.0)
Regular exercise
	Yes	18,434	(42.3)	945	(44.0)	0.126	14,208	(41.9)	5,171	(43.7)	0.001	18,826	(42.2)	553	(48.6)	< 0.001	13,056	(42.8)	6,323	(41.6)	0.023
	No	25,127	(57.7)	1,202	(56.0)		19,661	(58.1)	6,668	(56.3)		25,745	(57.8)	584	(51.4)		17,470	(57.2)	8,859	(58.4)
Physical activity
	Yes	21,483	(52.3)	1,001	(49.4)	0.010	16,487	(51.9)	5,997	(53.1)	0.022	21,894	(52.1)	590	(54.5)	0.129	15,121	(52.7)	7,363	(51.2)	0.004
	No	19,557	(47.7)	1,025	(50.6)		15,294	(48.1)	5,288	(46.9)		20,090	(47.9)	492	(45.5)		13,574	(47.3)	7,008	(48.8)
Walking speed
	Slow	18,183	(44.6)	994	(49.1)	< 0.001	14,157	(44.8)	5,020	(44.7)	0.840	18,689	(44.8)	488	(45.1)	0.847	12,776	(44.8)	6,401	(44.8)	0.981
	Fast	22,629	(55.4)	1,031	(50.9)		17,445	(55.2)	6,215	(55.3)		23,066	(55.2)	594	(54.9)		15,759	(55.2)	7,901	(55.2)
Eating speed
	Slow/Normal	32,197	(78.4)	1,396	(68.7)	< 0.001	24,832	(78.1)	8,761	(77.5)	0.215	32,793	(78.1)	800	(73.5)	< 0.001	22,551	(78.5)	11,042	(76.8)	< 0.001
	Fast	8,867	(21.6)	635	(31.3)		6,963	(21.9)	2,539	(22.5)		9,214	(21.9)	288	(26.5)		6,162	(21.5)	3,340	(23.2)
Eating before bedtime
	Yes	6,300	(15.3)	416	(20.5)	< 0.001	4,838	(15.2)	1,878	(16.6)	< 0.001	6,549	(15.6)	167	(15.4)	0.892	4,586	(16.0)	2,130	(14.8)	0.002
	No	34,758	(84.7)	1,618	(79.5)		26,959	(84.8)	9,417	(83.4)		35,458	(84.4)	918	(84.6)		24,122	(84.0)	12,254	(85.2)
Skipping breakfast
	Yes	3,182	(7.8)	197	(9.7)	0.002	2,546	(8.0)	833	(7.4)	0.035	3,328	(7.9)	51	(4.7)	< 0.001	2,180	(7.6)	1,199	(8.3)	0.007
	No	37,869	(92.2)	1,831	(90.3)		29,246	(92.0)	10,454	(92.6)		38,665	(92.1)	1,035	(95.3)		26,522	(92.4)	13,178	(91.7)
Drinking habits
	Yes	11,260	(26.2)	900	(42.3)	< 0.001	8,403	(25.2)	3,757	(32.2)	< 0.001	11,832	(27.0)	328	(28.8)	0.178	8,518	(28.3)	3,642	(24.3)	< 0.001
	No	31,643	(73.8)	1,228	(57.7)		24,964	(74.8)	7,907	(67.8)		32,060	(73.0)	811	(71.2)		21,549	(71.7)	11,322	(75.7)
Sufficient sleep
	Yes	31,367	(76.7)	1,590	(78.6)	0.043	24,180	(76.3)	8,777	(78.0)	< 0.001	32,097	(76.7)	860	(79.5)	0.034	22,043	(77.1)	10,914	(76.2)	0.041
	No	9,549	(23.3)	432	(21.4)		7,507	(23.7)	2,474	(22.0)		9,759	(23.3)	222	(20.5)		6,565	(22.9)	3,416	(23.8)

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^aP values are calculated using the chi-square test.

Table 2 presents the relationship between lifestyle behaviors and lifestyle diseases, determined using a logistic regression analysis. The results of the simple regression model and Model 1 are presented in Table 2. The results of Model 2, adjusted for sex, age, BMI, and laboratory values, are presented below (see Supplementary S1 Fig). MetS demonstrated positive associations with smoking, weight gain ≥10 kg since the age of 20 years, slow walking, and fast eating (Table 2A). The highest OR was observed for weight gain ≥10 kg since the age of 20 years (OR: 2.01, CI: 1.79–2.25). Hypertension was positively associated with smoking, weight gain ≥10 kg since the age of 20 years, the lack of regular exercise, slow walking, and problematic drinking habits (Table 2B). Among these, the highest OR was observed for problematic drinking habits (OR: 1.22, CI: 1.15–1.30). Diabetes was positively associated with fast eating (OR: 1.17, CI: 1.01–1.36). Conversely, skipping breakfast showed a negative relationship with diabetes (OR: 0.70, CI: 0.51–0.95) (Table 2C). Dyslipidemia was positively associated with smoking, weight gain ≥10 kg since the age of 20 years, skipping breakfast, and insufficient sleep. Conversely, drinking habits showed a negative association with dyslipidemia (OR: 0.90, CI: 0.85–0.95) (Table 2D).

Table 2. Correlation between lifestyle behaviors and lifestyle diseases determined using a logistic regression model.

		Univariate			multivariate
		Univariate			model1^a			model2^b
Lifestyle behaviors		OR	95%Cl	P value	OR	95%Cl	P value	OR	95%Cl	P value
A) Metabolic syndrome
Smoking	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.92	(1.74-2.13)	< 0.001	1.38	(1.22-1.56)	< 0.001	1.50	(1.32-1.70)	< 0.001
Weight gain of ≥10 kg since the age of 20 years	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	6.40	(5.85-7.02)	< 0.001	5.46	(4.95-6.03)	< 0.001	2.01	(1.79-2.25)	< 0.001
Regular exercise	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	0.93	(0.86-1.02)	0.120	0.94	(0.85-1.05)	0.307	0.96	(0.86-1.08)	0.505
Physical activity	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	1.12	(1.03-1.23)	0.010	1.05	(0.94-1.16)	0.418	1.03	(0.92-1.15)	0.638
Walking speed	Fast	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Slow	1.20	(1.10-1.31)	< 0.001	1.25	(1.13-1.38)	< 0.001	1.16	(1.05-1.30)	0.005
Eating speed	Slow/Normal	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Fast	1.65	(1.50-1.82)	< 0.001	1.27	(1.15-1.42)	< 0.001	1.10	(0.99-1.23)	0.090
Eating before bedtime	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.42	(1.27-1.59)	< 0.001	1.01	(0.90-1.15)	0.817	0.96	(0.84-1.09)	0.495
Skipping breakfast	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.28	(1.10-1.49)	0.001	0.98	(0.83-1.17)	0.859	1.05	(0.87-1.25)	0.615
Problematic drinking habits	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	2.06	(1.89-2.25)	< 0.001	1.18	(1.06-1.31)	0.003	1.14	(1.02-1.28)	0.024
Sufficient sleep	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	0.89	(0.80-0.99)	0.040	0.93	(0.83-1.05)	0.253	1.01	(0.90-1.15)	0.825
B) Hypertension
Smoking	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.13	(1.07-1.2)	< 0.001	1.11	(1.04-1.19)	0.003	1.18	(1.10-1.27)	< 0.001
Weight gain of ≥10 kg since the age of 20 years	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.44	(1.37-1.52)	< 0.001	1.44	(1.36-1.53)	< 0.001	1.15	(1.07-1.23)	< 0.001
Regular exercise	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	0.93	(0.89-0.97)	0.001	1.06	(1.01-1.12)	0.019	1.06	(1.01-1.12)	0.032
Physical activity	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	0.95	(0.91-0.99)	0.021	0.96	(0.91-1.00)	0.073	0.96	(0.91-1.01)	0.132
Walking speed	Fast	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Slow	1.00	(0.95-1.04)	0.832	1.06	(1.01-1.11)	0.012	1.06	(1.01-1.12)	0.019
Eating speed	Slow/Normal	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Fast	1.03	(0.98-1.09)	0.210	1.03	(0.97-1.08)	0.341	1.00	(0.94-1.06)	0.972
Eating before bedtime	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.11	(1.05-1.18)	< 0.001	1.08	(1.01-1.15)	0.016	1.05	(0.98-1.13)	0.135
Skipping breakfast	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	0.92	(0.84-0.99)	0.033	1.03	(0.94-1.13)	0.541	1.04	(0.94-1.14)	0.455
Problematic drinking habits	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.41	(1.35-1.48)	< 0.001	1.38	(1.31-1.46)	< 0.001	1.22	(1.15-1.30)	< 0.001
Sufficient sleep	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	0.91	(0.86-0.96)	< 0.001	0.96	(0.91-1.02)	0.189	0.99	(0.93-1.05)	0.747
C) diabetes
Smoking	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.30	(1.12-1.51)	0.001	1.14	(0.96-1.37)	0.132	1.17	(0.98-1.41)	0.086
Weight gain of ≥10 kg since the age of 20 years	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.79	(1.57-2.05)	< 0.001	1.62	(1.41-1.87)	< 0.001	1.11	(0.94-1.32)	0.222
Regular exercise	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	0.77	(0.69-0.87)	< 0.001	0.91	(0.79-1.05)	0.203	0.95	(0.82-1.10)	0.489
Physical activity	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	0.91	(0.80-1.03)	0.122	0.97	(0.85-1.12)	0.707	0.95	(0.83-1.10)	0.491
Walking speed	Fast	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Slow	1.01	(0.90-1.14)	0.823	1.14	(0.99-1.30)	0.050	1.13	(0.99-1.30)	0.075
Eating speed	Slow/Normal	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Fast	1.28	(1.12-1.47)	< 0.001	1.24	(1.08-1.44)	0.003	1.17	(1.01-1.36)	0.037
Eating before bedtime	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	0.98	(0.83-1.16)	0.859	0.98	(0.82-1.17)	0.839	0.96	(0.80-1.15)	0.639
Skipping breakfast	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	0.57	(0.43-0.76)	< 0.001	0.64	(0.48-0.87)	0.004	0.70	(0.51-0.95)	0.021
Problematic drinking habits	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.10	(0.96-1.25)	0.167	0.79	(0.68-0.92)	0.002	0.94	(0.81-1.10)	0.463
Sufficient sleep	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	0.85	(0.73-0.99)	0.032	0.95	(0.81-1.11)	0.500	0.95	(0.81-1.12)	0.547
D) Dyslipidemia
Smoking	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.07	(1.01-1.13)	0.018	1.16	(1.09-1.24)	< 0.001	1.26	(1.17-1.35)	< 0.001
Weight gain of ≥10 kg since the age of 20 years	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.31	(1.25-1.38)	< 0.001	1.35	(1.28-1.43)	< 0.001	1.17	(1.10-1.25)	< 0.001
Regular exercise	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	1.05	(1.01-1.09)	0.022	1.00	(0.96-1.05)	0.927	1.01	(0.96-1.06)	0.784
Physical activity	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	1.06	(1.02-1.10)	0.004	1.02	(0.97-1.07)	0.427	1.01	(0.96-1.06)	0.616
Walking speed	Fast	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Slow	1.00	(0.96-1.04)	0.973	0.98	(0.94-1.02)	0.296	0.98	(0.94-1.03)	0.431
Eating speed	Slow/Normal	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Fast	1.11	(1.06-1.16)	< 0.001	1.09	(1.04-1.15)	0.001	1.05	(0.99-1.11)	0.069
Eating before bedtime	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	0.91	(0.87-0.97)	0.002	0.93	(0.88-0.99)	0.019	0.95	(0.89-1.01)	0.125
Skipping breakfast	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	1.11	(1.03-1.19)	0.007	1.13	(1.04-1.23)	0.003	1.10	(1.01-1.20)	0.028
Problematic drinking habits	No	1.00	(reference)		1.00	(reference)		1.00	(reference)
	Yes	0.81	(0.78-0.85)	< 0.001	0.81	(0.77-0.86)	< 0.001	0.90	(0.85-0.95)	< 0.001
Sufficient sleep	Yes	1.00	(reference)		1.00	(reference)		1.00	(reference)
	No	1.05	(1.00-1.10)	0.040	1.02	(0.97-1.07)	0.504	1.06	(1.00-1.11)	0.045

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^a Model 1 included the 10 lifestyle behaviors as independent variables and was adjusted for sex and age as covariates.

^b Model 2 was adjusted for Model 1 by adding BMI and laboratory tests as covariates.

Discussion

This retrospective study analyzed 10 years of accumulated health checkup data to examine lifestyle behaviors associated with the development of MetS and its components—hypertension, diabetes, and dyslipidemia. To our knowledge, this study is the first to assess the impact of lifestyle behaviors on the development of lifestyle diseases using such an extensive dataset, which enhances the statistical power and reliability of the findings. The assessment accurately calculated the positive measurement test results and accounted for the interaction effects of lifestyle behaviors on the onset of lifestyle diseases. New insights were obtained using a two-stage multivariable logistic regression model, where Model 1 and Model 2 were sequentially adjusted to account for potential confounding variables.

First, we identified an association between diabetes and lifestyle behaviors. Smoking [21–23], weight gain of ≥10 kg since the age of 20 years [24,25], the lack of regular exercise [26,27], slow walking [23], fast eating [14,17,23,28,29], and adopting poor sleeping habits [23] have been reported as risk factors for diabetes. Our study showed that fast eating was the sole risk factor in Model 2, while three factors were identified in Model 1 (Table 2C). The remaining two factors, weight gain of ≥10 kg since the age of 20 years and problematic drinking habit, were significantly associated with measurement tests that influence the onset of diabetes and may serve as confounders in this relationship [30,31]. Notably, our study showed that skipping breakfast protects against the onset of diabetes. Previous studies have reported a positive correlation between skipping breakfast and diabetes onset [15,16], whereas others have reported no correlation [17]. Our research suggests a negative correlation between skipping breakfast and diabetes onset, which contradicts those of previous studies and presents new insights into this issue. A previous study has highlighted the importance of considering both energy intake and dietary content [26]. However, the unique dietary habits in the Gunma Prefecture may have influenced this result. According to the NHNS in 2016, despite similar daily energy intake, the residents of Gunma Prefecture did consume significantly more carbohydrates compared with the national average, potentially affecting glucose metabolism and insulin sensitivity. Additionally, meal timing and overall dietary patterns could play a role. A previous study suggest that prolonged fasting intervals may improve metabolic health by enhancing insulin sensitivity and glycemic control [32]. It is possible that individuals who skip breakfast in this population may compensate with well-balanced meals later in the day, mitigating the adverse effects typically associated with breakfast skipping. Nonetheless, alternative explanations should be considered. For example, reverse causation may partly explain this finding—for instance, individuals with preclinical diabetes may have modified their eating habits by skipping breakfast to manage symptoms or weight. In addition, since dietary habits were self-reported, reporting bias may have influenced the observed association. Furthermore, our study was limited to insured adults within Gunma Prefecture, which may restrict the generalizability of our findings. Finally, unmeasured confounders such as sleep patterns, work schedules, and socioeconomic status could also have affected the results. Taken together, these factors highlight the need for cautious interpretation of our findings and suggest that further studies using objective dietary assessments and longitudinal data are warranted to clarify these associations.

Second, we identified an association between MetS and lifestyle behaviors. Previous studies have reported smoking [33], weight gain ≥10 kg since the age of 20 years [34], slow walking [13], fast eating [35,36], skipping breakfast (for men) [37], problematic drinking habits [13,38,39], and adopting poor sleeping habits (for men) [37] as MetS risk factors. Our study showed that smoking, weight gain ≥10 kg since the age of 20 years, slow walking, and problematic drinking habits were risk factors in Model 2, while five factors were identified in Model 1 (Table 2A). The remaining factor, fast eating, might be associated with measurement tests that influence the onset of MetS and previous study have suggested the need for adjustments based on these test items [10].

Third, we identified an association between hypertension and lifestyle behaviors. Previous studies have reported smoking [23,40], weight gain ≥10 kg since the age of 20 years [34], the lack of regular exercise [26], slow walking [23], fast eating [23], problematic drinking habits [39–41], and adopting poor sleeping habits [23] as hypertension risk factors. Our study showed that smoking, weight gain of ≥10 kg since the age of 20 years, the lack of regular exercise, slow walking, and problematic drinking habits were risk factors in Model 2, while six factors were identified in Model 1 (Table 2B). The remaining factor, eating before bedtime, might be associated with measurement tests that influence the onset of hypertension and previous study have suggested the need for adjustments based on these test items [11].

Fourth, we identified an association between dyslipidemia and lifestyle behaviors. Previous studies have reported smoking [42], weight gain ≥10 kg since the age of 20 years [34], skipping breakfast [43], and adopting poor sleeping habits [44] as dyslipidemia risk factors. These lifestyle behaviors were similarly confirmed as risk factors in Model 2, while six factors were identified in Model 1 (Table 2D). The remaining two factors, fast eating and eating before bedtime might be associated with measurement tests that influence the onset of dyslipidemia and previous studies have suggested the need for adjustments based on these test items [11,36]. Additionally, our study showed that drinking habits are protective against the onset of dyslipidemia, which aligns with previous research finding [39].

Thus, we showed that utilizing the results of self-administered questionnaires from SHC is effective in preventing lifestyle diseases. Notably, smoking and weight gain ≥10 kg since the age of 20 years were identified as common risk factors for MetS, hypertension, and dyslipidemia. The findings of this study provide essential evidence to support and strengthen targeted public health interventions, such as smoking cessation programs and weight management strategies based on the Health Promotion Act. Addressing these two factors through such interventions could play a crucial role in disease prevention and risk reduction.

The impact of lifestyle factors such as smoking, weight management, physical activity, and alcohol consumption on cardiovascular disease and life expectancy has been widely recognized across different populations [45,46]. However, cultural and dietary differences may influence these associations. Our study, which adjusted for 10 detailed lifestyle habits, offers a broader perspective than previous studies. Further research in diverse populations is needed to confirm these findings.

This study has several limitations. First, we did not account for any changes in lifestyle behaviors among individuals who underwent SHCs during the 4-year period following the initial health checkup. Longitudinal studies often struggle to incorporate the changes in background factors into their models; therefore, future research incorporating longitudinal tracking of lifestyle behaviors to account for these changes is necessary. Second, the lifestyle assessment data collected using self-administered questionnaires lack objectivity and are subject to potential biases, such as recall bias and social desirability bias. However, this method has been validated in studies by Takeuchi et al. [47] and Fukasawa et al. [19], demonstrating its statistical validity. To mitigate potential biases, future studies should consider complementing self-reported data with objective measures, such as wearable devices for physical activity monitoring. Third, the database used comprised information of individuals insured under the Gunma Prefecture’s NHI. Consequently, it remains unclear whether these findings can be generalized to other regions or insurance groups. Regional characteristics, economic indicators, and the social determinants of health may influence the outcomes. Future studies in diverse populations and healthcare systems are needed to validate these findings and ensure broader applicability.

In conclusion, this study elucidated the time-line changes associations between lifestyle behaviors and MetS, hypertension, diabetes, and dyslipidemia using SHC data accumulated over 10 years. Additionally, the correlations between lifestyle-related diseases and specific lifestyle habits were evaluated. These results suggest that self-administered questionnaires assessing lifestyle behaviors are useful for predicting future health issues. Notably, smoking and weight gain emerged as common risk factors for these four diseases, highlighting the need for targeted interventions to address these issues.

Supporting information

S1 Table. Cutoff values.

(XLSX)

pone.0327505.s001.xlsx^{(25.3KB, xlsx)}

S2 Table. List of “standard questionnaire”.

(XLSX)

pone.0327505.s002.xlsx^{(12.8KB, xlsx)}

S3 Table. BMI, and laboratory values of individuals who developed and did not develop lifestyle diseases.

Supplemental data for Table 1.

(XLSX)

pone.0327505.s003.xlsx^{(15.2KB, xlsx)}

S1 Fig. Correlation between lifestyle behaviors and lifestyle diseases determined using a multivariable logistic regression model (Supplementary to Table 2).

The odds ratio (black bars) with corresponding 95% confidence intervals (error bars) is shown for each category of lifestyle diseases. *P < 0.05, **P < 0.01, ***P < 0.001 compared with the reference category.

(TIFF)

pone.0327505.s004.tiff^{(584.1KB, tiff)}

Acknowledgments

We express our gratitude to the Gunma Prefecture Health and Welfare Department, Health and Longevity Society Promotion Section, for their invaluable support in facilitating the use of the Gunma Prefecture National Health Insurance data. We would like to thank Editage (www.editage.jp) for English language editing.

Data Availability

The Ethics Committee for Research Involving Humans of the Gunma University Faculty of Medical applies the restriction for public data sharing due to ethical and legal restrictions of the annual health check-up data containing sensitive information. De-identified data may be available upon reasonable request and subject to approval by the Ethics Committee. Requests for data access should be directed to the Office of the Ethics Committee for Research Involving Humans, Advanced Medical Development Center, Gunma University (Email: hitotaisho-ciru@ml.gunma-u.ac.jp).

Funding Statement

TS received funding from JST (Japan Science and Technology Agency), Grant Number JPMJPF2301.

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PLoS One. doi: 10.1371/journal.pone.0327505.r001

Decision Letter 0

Mulu Tiruneh

PONE-D-24-56824Impact of Lifestyle Behaviors on the Development of Lifestyle Diseases: A Retrospective Cohort StudyPLOS ONE

Dear Dr. Saitoh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 19 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
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We look forward to receiving your revised manuscript.

Kind regards,

Mulu Tiruneh

Academic Editor

PLOS ONE

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We will update your Data Availability statement on your behalf to reflect the information you provide.

3. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript presents a comprehensive and well-executed study on the impact of lifestyle behaviors on the development of lifestyle diseases using a robust dataset from a large retrospective cohort. The research is technically sound, with an appropriately designed methodology and statistical analysis that strengthens the reliability of the findings. By utilizing logistic regression models with stepwise adjustments, the study effectively accounts for key confounders, including age, sex, BMI, and laboratory parameters. The large sample size of over 47,000 participants ensures sufficient statistical power, and the stratified analysis of disease outcomes enhances the depth of the results.

The statistical analysis is rigorous, employing multivariate models that explore the independent effects of lifestyle behaviors. However, additional details on multicollinearity diagnostics, such as reporting variance inflation factors (VIFs), would further validate the results. While the use of odds ratios with confidence intervals is appropriate, the discussion could benefit from emphasizing the clinical relevance of statistically significant findings. For instance, while smoking and weight gain emerged as common risk factors across multiple diseases, it would be helpful to contextualize these findings within public health interventions.

One intriguing finding is the protective association between skipping breakfast and diabetes, which contradicts much of the existing literature. While this result is novel, it warrants further exploration, possibly through sensitivity or subgroup analyses to account for regional dietary habits or meal composition. The manuscript would also benefit from a discussion on residual confounding, as factors like socioeconomic status or genetic predispositions were not included in the analysis.

Although the reliance on self-reported data is acknowledged as a limitation, it introduces potential biases such as recall and social desirability bias. A more detailed discussion on how these biases may have influenced the results would strengthen the manuscript. Additionally, the study does not account for changes in lifestyle behaviors during the follow-up period, which may affect the observed associations. Future research should consider longitudinal tracking of behavioral changes to provide a more nuanced understanding of their effects.

In terms of generalizability, the study is limited to a specific region in Japan, and the findings may not be directly applicable to other populations with different cultural, dietary, or healthcare contexts. The authors should discuss how similar studies in other regions could validate and expand upon these results.

In conclusion, this manuscript is a valuable contribution to the field of public health and provides actionable insights into the relationship between lifestyle behaviors and lifestyle diseases. With minor improvements in the discussion of limitations, clinical relevance, and generalizability, this study could serve as a strong foundation for public health strategies aimed at mitigating the burden of lifestyle diseases.

Kudos to authors and team member in developing a very good project and article.

Reviewer #2: Strengths:

The study addresses a critical public health issue based on a large longitudinal dataset.

The application of the logistic regression model is appropriate, and the results are presented clearly.

The supplementary materials included herein enhance the reproducibility of the study.

Suggestions for Improvement:

Explain why the analysis excluded individuals who had multiple checkups. If this is done, selection bias could be introduced into the analysis because such persons may differ in some systematic way from those included in the analysis.

Address the multicollinearity issue in the logistic regression models by reporting the VIF values and discussing how the overlapping lifestyle behaviors were dealt with.

Simplify and visualize results using plots of bar charts or heat maps that give in one glance several key findings of Table 1 and Table 2.

The findings should be discussed for their clinical significance. However, not every statistically significant association is clinically significant and may deserve further consideration in terms of modest effect size, such as the identified one for fast eating and diabetes, with an OR of 1.17. Expand the discussion of limitations. Acknowledge potential biases (e.g., recall bias, selection bias) and the limited generalizability of the findings.

Highlight practical implications: Describe how such findings might be used to inform public health interventions or policy.

Additional Comments:

The negative relation of breakfast omission with diabetes appears intriguingly protective, with an odds ratio of 0.70, which also goes against previous reports. This might be discussed more elaborately in the discussion part, considering either regional dietary habits or confounding. Where relevant, for example, when referring to the "standard questionnaire" or cutoff values, the manuscript should refer explicitly to supplementary tables.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: Yes: Kola Adegoke

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Attachment

Submitted filename: COMMENTS ON ARTICLE.pdf

pone.0327505.s005.pdf^{(62.8KB, pdf)}

PLoS One. 2025 Jul 29;20(7):e0327505. doi: 10.1371/journal.pone.0327505.r002

Author response to Decision Letter 1

17 Apr 2025

We appreciate the constructive feedback from the reviewers and the editor. We have carefully revised the manuscript in response to their comments. A detailed point-by-point response is provided in the attached document.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0327505.s007.docx^{(68.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0327505.r003

Decision Letter 1

Mulu Tiruneh

PONE-D-24-56824R1Impact of Lifestyle Behaviors on the Development of Lifestyle Diseases: A Retrospective Cohort StudyPLOS ONE

Dear Dr. Saitoh,

Please submit your revised manuscript by Jul 04 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mulu Tiruneh

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: We appreciate the chance to comment on the revised manuscript entitled "Impact of Lifestyle Behavior on Lifestyle Disease Development: A Retrospective Cohort Study." The study is well-timed and methodologically rigorous, using a large dataset to assess self-reported lifestyle behavior predictors for the development of metabolic syndrome, diabetes, hypertension, and dyslipidemia. The statistical strategy, which involves logistic regression models and stepwise adjustments, is suitable and well-explained. The authors enhance the manuscript further through a more explicit elaboration of rationale and substantiation for the relationship between behavioral surveillance and prediction of public health risk.

However, I want to voice my concern over the findings' interpretability and generalizability. The implication that skipping breakfast is linked to less diabetes contradicts the literature. It suggests a more elaborate sensitivity analysis or other explanations, such as reverse causation or bias for reporting. Although the data are extensive, the sample is geographically and demographically restricted (only insured adults within Gunma Prefecture), and this may be better highlighted. The confounding effect from unmeasured lifestyles or socioeconomic factors (e.g., sleep, work, job status) could also be mentioned.

Finally, whilst the manuscript is composed well and readable, the Data Availability Statement is inconsistent with PLOS’s open data policy. Please specify a means for de-identified data availability through a recognized repository or institutional arrangements. I suggest minor revisions and changes to meet these issues and enhance the manuscript's contribution to the evidence base for preventive health actions through behavioral data.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #2: Yes: Kola Adegoke

**********

PLoS One. 2025 Jul 29;20(7):e0327505. doi: 10.1371/journal.pone.0327505.r004

Author response to Decision Letter 2

11 Jun 2025

We thank the editor and reviewers for their thoughtful and constructive comments. We have carefully revised the manuscript in response to all points raised. A detailed point-by-point response is provided in the attached response letter.

Attachment

Submitted filename: Response to Revie wer_ver2.docx

pone.0327505.s008.docx^{(28.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0327505.r005

Decision Letter 2

Mulu Tiruneh

Impact of Lifestyle Behaviors on the Development of Lifestyle Diseases: A Retrospective Cohort Study

PONE-D-24-56824R2

Dear Dr. Saitoh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Mulu Tiruneh

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0327505.r006

Acceptance letter

Mulu Tiruneh

PONE-D-24-56824R2

PLOS ONE

Dear Dr. Saitoh,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Mr. Mulu Tiruneh

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Cutoff values.

(XLSX)

pone.0327505.s001.xlsx^{(25.3KB, xlsx)}

S2 Table. List of “standard questionnaire”.

(XLSX)

pone.0327505.s002.xlsx^{(12.8KB, xlsx)}

S3 Table. BMI, and laboratory values of individuals who developed and did not develop lifestyle diseases.

Supplemental data for Table 1.

(XLSX)

pone.0327505.s003.xlsx^{(15.2KB, xlsx)}

S1 Fig. Correlation between lifestyle behaviors and lifestyle diseases determined using a multivariable logistic regression model (Supplementary to Table 2).

(TIFF)

pone.0327505.s004.tiff^{(584.1KB, tiff)}

Attachment

Submitted filename: COMMENTS ON ARTICLE.pdf

pone.0327505.s005.pdf^{(62.8KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0327505.s007.docx^{(68.4KB, docx)}

Attachment

Submitted filename: Response to Revie wer_ver2.docx

pone.0327505.s008.docx^{(28.2KB, docx)}

Data Availability Statement

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PERMALINK

Impact of lifestyle behaviors on the development of lifestyle diseases: A retrospective cohort study

Takafumi Okawa

Hikaru Negishi

Yuki Aoki

Mitsuo Uchida

Yumi Sato

Mai Ishikawa

Rie Matsui

Kaori Hotta

Takayuki Saitoh

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

Database

Study population

Fig 1. Study population.

Variables

Statistical analysis

Ethical considerations

Results

Table 1. Baseline characteristics of individuals who developed and did not develop lifestyle diseases.

Table 2. Correlation between lifestyle behaviors and lifestyle diseases determined using a logistic regression model.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Mulu Tiruneh

Roles

Author response to Decision Letter 1

Decision Letter 1

Mulu Tiruneh

Roles

Author response to Decision Letter 2

Decision Letter 2

Mulu Tiruneh

Roles

Acceptance letter

Mulu Tiruneh

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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