Metabolic syndrome (MetS) and cancer are major public health problems worldwide. The relationship between MetS and cancer death is of great interest. We examined the predictive value of MetS for cancer mortality in Japan.
Methods
Study participants included 4495 men and 7028 women aged 18–90 years who were registered between 1992 and 1995 as part of the Jichi Medical School Cohort Study. We used a definition of MetS modified for the Japanese population. The primary outcome was cancer mortality. Additionally, the relationship between MetS and cancer-type specific mortality was examined. Analyses were conducted with Cox’s regression models adjusted for age, smoking status, alcohol drinking status, marital status, educational attainment, physical activity, occupational category, and menopausal status (only in women).
Results
During a mean follow-up of 18.5 years, 473 men and 297 women died from cancer. MetS was positively associated with cancer mortality in women (hazard ratio [HR], 1.69; 95% confidence interval [CI] 1.21–2.36), but not in men (HR, 1.21; 95% CI 0.90–1.62). Additionally, MetS was associated with a high risk of colorectal (HR, 3.48; 95% CI 1.68–7.22) and breast (HR, 11.90; 95% CI 2.25–62.84) cancer deaths in women.
Conclusion
MetS was a significant predictor of cancer mortality in women.
International Classification of Diseases 10th revision
WC
waist circumference
SD
standard deviation
IQR
interquartile range
NCEP-ATP
National Cholesterol Education Program Adult Treatment Panel
JPHC
the Japan Public Health Center-based prospective study
JACC
the Japan Collaborative Cohort Study
IDF
International Diabetes Federation
IGF
insulin-like growth factor
VAT
visceral adipose tissue
FFAs
free fatty acids
Background
Metabolic syndrome (MetS) is a disease characterized by a cluster of high blood glucose, dyslipidemia, obesity, and hypertension [1]. MetS is an important risk factor for not only cardiovascular diseases (CVD) but also the development of cancer [2, 3]. Accumulating evidence regarding the clinical value of MetS in estimating the risk of cancer has led to increased interest in the relationship between MetS and cancer.
Cancer remains a major cause of death worldwide, with 14.1 million new cases and 8.2 million deaths from cancer occurring annually [4]. Of note, cancer deaths in Japan have been gradually increasing and now constitute the leading cause of death in the country [5]. Each component of MetS, viz., obesity [6], hypertension [7], hyperglycemia [8‐11], and dyslipidemia [12], independently increases the risk of cancer. However, it remains unclear whether there is a dose–response association between MetS components and cancer mortality. Despite substantial interest in the relationship between MetS and cancer deaths, few studies have examined the contribution of the syndrome to cancer deaths [13‐17].
Anzeige
We herein investigated the relationship between MetS and cancer mortality in a general Japanese population.
Methods
Participants and follow-up
The present study was a serial prospective population-based cohort analysis using data from the Jichi Medical School (JMS) Cohort Study. The research design of the JMS Cohort Study and some descriptive data have been reported in detail elsewhere [18]. The study was initiated in 1992 to investigate the relationship between potential risk factors and CVD in the general Japanese population. Baseline data in 12 Japanese communities were obtained between April 1992 and July 1995 from national mass screening examinations for CVD, which were conducted according to the Health and Medical Service Law for the Aged in Japan. Local municipal governments sent personal invitations through the mail to all mass screening participants. Participants were aged 40–69 years in eight areas (Iwaizumi, Tako, Kuze, Sakuma, Sakugi, Okawa, Ainoshima, and Akaike), ≥ 35 years in one area (Wara), and ≥ 18 years in three areas (Hokudan, Yamato, and Takasu). We included 12,490 participants (4911 men and 7579 women), and the follow-up rate was 99% in 18 years from the data of baseline registration to the end of 2013. After the exclusion of 889 participants, including those who were lost to follow-up (n = 97), had a history of cancer (n = 141), had missing data for height and weight (n = 494) or blood pressure and blood samples (n = 157), or died in the first 2 years of follow-up (n = 78), the remaining 11,523 participants (4495 men and 7028 women) were eligible for the analysis.
The JMS Cohort Study conducted follow-up surveys until 31 December 2013. We obtained death certificates from public health centers with the permission of the Agency of General Affairs and the Ministry of Health, Labour and Welfare. Each municipal government collected annual data on participant relocation. In Japan, the registries of residency and deaths are established by law and doctors are trained to describe in standard format.
Measurements and outcomes
In the baseline survey, height without shoes and weight of clothed participants were measured, and body mass index (BMI) was calculated as weight (kg)/height (m)2. Trained interviewers used a standardized questionnaire to obtain data, including smoking habit (never, past, or current smoker), alcohol drinking habit (never, past, or current drinker), medical history (past or present hypertension, diabetes, and hyperlipidemia, and the presence of these medication), marital status (yes or no), educational attainment (the age at completion of education), physical activity (the Framingham Study Questionnaire [19]), occupation, and menopause status (pre or post) in women. Educational attainment was categorized into less than junior high school (≤ 15 years), high school (16–18 years), and more than high school (≥ 19 years). Physical activity was categorized by using physical activity index (PAI) estimated by calculating the coefficients and time spent on an activity, into low (PAI < 30), middle (PAI = 30–39), and high (PAI ≥ 40) in this study [19‐21]. Occupation was categorized into white-collar, blue-collar, or no working. Sales workers, clerks, professional/technicians, and service workers were categorized as white-collar occupations. Agriculture and forestry, fishery, security, transportation/communications, civil engineering and construction, and craft workers/laborers were categorized as blue-collar occupations, while retiree and inoccupation were categorized as no working [22]. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using a fully automated sphygmomanometer, BP203RV-II (Nippon Colin Co., Ltd., Komaki, Japan), on the right arm in the sitting position after at least 5 min of rest. Serum total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and plasma glucose (PG) concentrations were measured using enzymatic methods, as previously reported [18].
Anzeige
Information on the causes of death were determined by death certificates and coded using the International Classification of Diseases 10th revision (ICD-10). The primary endpoint was total cancer deaths (C00–C97), and secondary outcomes were lung (C33–34), stomach (C16), colon (C18), rectum (C19–20), liver (C22), gallbladder (C23), prostate (C61), and breast (C50) cancer deaths.
Definition
We applied the modified Japanese MetS definition using BMI instead of waist circumference (WC) because only approximately 20% of all participants in the JMS Cohort Study had WC measured, and BMI ≥ 25 kg/m2 is consistent with a WC of ≥ 85 cm in men and ≥ 90 cm in women in Japan [23]. MetS was defined as BMI ≥ 25 kg/m2 and the presence of two or more of the following: (i) SBP and/or DBP ≥ 130/85 mmHg or the use of antihypertensive medication; (ii) TG ≥ 1.69 mmol/L (150 mg/dL) and/or HDL-C < 1.03 mmol/L (40 mg/dL) and/or the use of antihyperlipidemic medication; and (iii) fasting PG ≥ 6.1 mmol/L (110 mg/dL) (with a fasting duration of at least 3 h) or casual PG (for less than 3 h or without regard to the time since the last meal) ≥ 7.8 mmol/L (140 mg/dL) and/or the use of antidiabetic medication.
Statistical analysis
Summary statistics were used to compare the characteristics of participants with and without MetS using the Mann–Whitney U test and χ2 test. To elucidate the relationship between MetS and cancer mortality, a Cox’s proportional hazards regression model was constructed to estimate multivariate-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for cancer mortality to the number of metabolic risk factors, the obesity category (BMI ≥ 25 kg/m2 or < 25 kg/m2), and MetS by sex, adjusting for age, smoking status (never, past, or current smoker), alcohol drinking status (never, past, or current drinker), marital status (yes or no), educational attainment (≤ 15, 16–18, or ≥ 19 years), physical activity (low, middle, high), occupation category (white-collar, blue-collar, or no working), and menopausal status (pre or post) only in women at baseline. These covariates are commonly adjusted for in cancer risk. However, tests for linear trend across the number of metabolic risk factors were conducted by including an ordinal scoring in the models to examine a dose–response association between MetS component and cancer mortality. The proportional hazards assumption for the model was checked by examining the log-negative-log plot of the survival function for participants with and without MetS, and the number of MetS components against time to death/follow-up time. These curves help in identifying non-proportionality patterns in hazard function such as crossing of the curves, convergent, or divergent. Additionally, we conducted Cox’s regression analysis by age ≥ 65 years or < 65 years and estimated multivariate adjusted HRs for cancer mortality to each metabolic risk factor, and for cancer-type specific mortality associated with MetS by sex. We performed sensitivity analyses by excluding participants who were younger than 40 years at baseline to minimize the influence of a younger generation. The threshold for significance was P < 0.05. All statistical analyses were conducted using IBM SPSS version 25.0 (IBM Corp., Armonk, NY, USA).
Results
The baseline characteristics of subjects with and without MetS are summarized for both the sexes in Table 1. The mean follow-up period was 18.5 (standard deviation [SD], 4.6) years. The median age of participants was 58 (interquartile range [IQR], 46–64) years in men and 57 (IQR, 47–64) years in women, and 91.3% of participants were older than 40 years. At baseline, 11.6% of men and 8.9% of women had MetS. There were no significant differences in smoking in men and women and alcohol drinking in men between participants with and without MetS. Both men and women with MetS had higher BMI, SBP, DBP, PG, TC and TG levels and lower HDL-C levels, compared to without MetS.
Table 1
Baseline characteristics of participants with or without metabolic syndrome by sex
Men
Pa
Women
Pa
Without MetS
With MetS
Without MetS
With MetS
N
%
N
%
N
%
N
%
Number of participants
3973
88.4
522
11.6
6406
91.1
622
8.9
N
Median (IQR)
N
Median (IQR)
Pa
N
Median (IQR)
N
Median (IQR)
Pa
Age (year)
58 (45–64)
56 (46–63)
0.016
57 (47–64)
60 (53–65)
< 0.001
BMI (kg/m2)
22.4 (20.8–24.0)
26.6 (25.8–28.1)
< 0.001
22.6 (20.8–24.4)
27.0 (25.9–28.9)
< 0.001
SBP (mmHg)
128 (115–141)
144 (135–156)
< 0.001
124 (112–139)
143 (134–156)
< 0.001
DBP (mmHg)
77 (70–86)
88 (82–94)
< 0.001
75 (67–83)
86 (80–92)
< 0.001
Plasma Glucose (mmol/L)
5.4 (4.9–6.1)
6.0 (5.3–7.1)
< 0.001
5.3 (4.9–5.8)
6.0 (5.2–7.1)
< 0.001
Total cholesterol (mmol/L)
4.7 (4.2–5.3)
5.1 (4.4–5.7)
< 0.001
5.0 (4.4–5.6)
5.4 (4.8–6.0)
< 0.001
HDL-cholesterol (mmol/L)
1.2 (1.1–1.5)
1.0 (0.9–1.2)
< 0.001
1.3 (1.2–1.6)
1.1 (1.0–1.3)
< 0.001
Triglyceride (mmol/L)
1.1 (0.8–1.6)
2.1 (1.5–2.8)
< 0.001
1.0 (0.7–1.4)
1.9 (1.4–2.5)
< 0.001
Smoking
%
%
%
%
Current
1946
49.0
230
44.1
0.101
336
5.3
30
4.8
0.345
Former
1062
26.7
149
28.5
174
2.7
11
1.8
Never
804
20.2
120
23.0
5535
86.4
541
87.1
Data missing
161
4.1
23
4.4
361
5.6
40
6.3
Alcohol drinking
Current
2811
70.8
343
65.7
0.210
1488
23.2
120
19.3
0.022
Former
123
3.1
22
4.2
78
1.2
14
2.2
Never
788
19.8
107
20.5
4353
68.0
411
66.1
Data missing
251
6.3
50
9.6
487
7.6
77
12.4
Diabetes mellitus
83
2.1
22
4.2
0.002
67
1.0
43
6.9
< 0.001
Hypertension
334
8.4
99
19.0
< 0.001
619
9.7
205
33.0
< 0.001
Hyperlipidemia
43
1.1
11
2.1
0.002
93
1.5
49
7.9
< 0.001
Marital status
Married
3644
91.7
473
90.6
0.278
5867
91.6
570
91.6
0.957
Single
310
7.8
48
9.2
519
8.1
50
8.0
Data missing
19
0.5
1
0.2
20
0.3
2
0.3
Education
≤ 15 years
1766
44.5
224
42.9
0.730
3176
49.6
364
58.5
< 0.001
16–18 years
1683
42.4
231
44.3
2558
39.9
213
34.2
≥ 19 years
503
12.7
67
12.8
650
10.1
43
6.9
Data missing
21
0.5
0
0
22
0.3
2
0.3
Physical activity
Low
1232
31.0
204
39.1
0.001
2813
43.9
276
44.4
0.321
Middle
1824
45.9
211
40.4
2942
45.9
293
47.1
High
730
18.4
81
15.5
335
5.2
24
3.9
Data missing
187
4.7
26
5.0
316
4.9
29
4.7
Occupation
White-collar
869
21.9
142
27.2
0.002
1491
23.3
125
20.1
< 0.001
Blue-collar
2548
64.1
296
56.7
2296
35.8
192
30.9
No working
532
13.4
84
16.1
2596
40.5
303
48.7
Data missing
24
0.6
0
0
23
0.4
2
0.3
MetS metabolic syndrome, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, HDL high-density lipoprotein, IQR interquartile range
aThe Mann–Whitney U test or χ2 test were performed
Figure 1 shows adjusted hazard curves of cancer mortality with the number of MetS components by Cox regression analysis. The proportional hazards assumption for the model was reasonable because the log-negative-log plot showed the separate lines did not cross and were not convergent, and divergent in Additional file 1: Figure S1. Table 2 shows HRs and 95% CIs for cancer mortality with the number of Japanese MetS components and obesity category. Increases in the number of Japanese MetS components showed a linear association with the HRs for cancer mortality (P for trend = 0.007), especially in women (P for trend = 0.027), but not in men (P for trend = 0.10). The effects of obesity with 2–3 metabolic risk factors were significantly greater than those in participants who were not obese and had no risk factors, whereas the effects of not being obese but having 2–3 risk factors were not, especially in women.
Fig. 1
Adjusted hazard curves of cancer mortality with the number of metabolic syndrome components
Table 2
Multivariate analysis of cancer mortality with the number of metabolic syndrome components
Participants
Cancer deaths
Person-years
Crude mortality (/1000 person-years)
Total HR-ageb (95% CI)
Total HR-allc (95% CI)
Men HR-allc (95% CI)
Women HR-allc (95% CI)
No. of metabolic risk factorsa
0
3439
173
64,584
2.7
1.00
1.00
1.00
1.00
1
4111
269
75,684
3.6
1.02 (0.84–1.24)
0.92 (0.76–1.12)
0.95 (0.73–1.23)
0.88 (0.64–1.20)
2
2628
212
48,329
4.4
1.17 (0.95–1.43)
1.10 (0.89–1.36)
1.14 (0.87–1.49)
1.04 (0.74–1.46)
3
1149
90
21,050
4.3
1.23 (0.95–1.59)
1.19 (0.92–1.55)
1.08 (0.76–1.54)
1.37 (0.92–2.05)
4
196
26
3512
7.4
2.14 (1.41–3.23)
1.91 (1.23–2.96)
1.68 (0.95–2.95)
2.32 (1.16–4.66)
P for trend
0.002
0.007
0.103
0.027
Combination of obesity and 3 other risk factors
Non-obesity and 0–1 risk factors
7077
422
131,208
3.2
1.00
1.00
1.00
1.00
Non-obesity and 2 risk factors
1451
136
26,162
5.2
1.25 (1.03–1.52)
1.18 (0.97–1.45)
1.28 (1.02–1.62)
0.89 (0.60–1.34)
Non-obesity and 3 risk factors
201
15
3499
4.3
1.04 (0.62–1.74)
0.98 (0.57–1.67)
0.99 (0.53–1.88)
0.90 (0.34–2.45)
Obesity and 0–1 risk factors
1650
96
31,268
3.1
0.93 (0.74–1.16)
1.04 (0.82–1.32)
0.85 (0.58–1.22)
1.28 (0.93–1.76)
Obesity and 2 risk factors
948
75
17,560
4.3
1.24 (0.97–1.59)
1.32 (1.02–1.71)
1.14 (0.80–1.61)
1.61 (1.10–2.36)
Obesity and 3 risk factors
196
26
3512
7.4
2.08 (1.40–3.10)
1.99 (1.31–3.04)
1.71 (0.99–2.95)
2.50 (1.27–4.92)
HR hazard ratio, CI confidence interval
aThe 4 components of being obesity, having an elevated blood pressure, elevated plasma glucose, and dyslipidemia
bHazard ratios adjusted for age
cHazard ratios adjusted for age, smoking status (never, past, or current smoker), alcohol drinking status (never, past, or current drinker), marital status (yes or no), educational attainment (≤ 15, 16–18, or ≥ 19 years), physical activity (low, middle, high), occupation category (white-collar, blue-collar, or no working), and menopausal status (pre or post) only in women
×
Figure 2 shows adjusted hazard curves of cancer mortality with metabolic syndrome by Cox regression analysis. The proportional hazard assumption for the model was reasonable in Additional file 1: Figure S2. Table 3 shows the number of deaths, crude mortality rates, and adjusted HRs for cancer mortality by sex. During the follow-up period, 770 deaths due to cancer (473 men and 297 women) occurred. Age-adjusted HRs were 1.11 (95% CI 0.84–1.48) in men and 1.69 (95% CI 1.23–2.31) in women. Multivariate-adjusted HRs were 1.21 (95% CI 0.90–1.62) in men and 1.69 (95% CI 1.21–2.36) in women. In addition, among women younger than 65 years, MetS was associated with a significantly increased risk of cancer mortality (multivariate-adjusted HR 1.66; 95% CI 1.09–2.55), whereas among women older than 65 years, there was no relationship between MetS and cancer mortality (multivariate-adjusted HR 1.69; 95% CI 0.99–2.89).
Fig. 2
Adjusted hazard curves of cancer mortality with metabolic syndrome by sex
Table 3
Multivariate analysis of cancer mortality with metabolic syndrome by sex
Men
Women
Without MetS
With MetS
Without MetS
With MetS
MetS participants, n (%)
3973 (88.4)
522 (11.6)
6406 (91.1)
622 (8.9)
Cancer deaths
418
55
251
46
Parson-Years
71,444
9418
120,718
11,654
Cancer mortality
Crude mortality (/1000 person-years)
5.9
5.8
2.1
3.9
HR-Agea (95% CI)
1.0 (reference)
1.11 (0.84–1.48)
1.0 (reference)
1.69 (1.23–2.31)
HR-Allb (95% CI)
1.0 (reference)
1.21 (0.90–1.62)
1.0 (reference)
1.69 (1.21–2.36)
< 65 years old
Crude mortality (/1000 person-years)
3.3
3.8
1.3
2.4
HR-Agea (95% CI)
1.0 (reference)
1.14 (0.80–1.61)
1.0 (reference)
1.70 (1.14–2.55)
HR-Allb (95% CI)
1.0 (reference)
1.22 (0.84–1.77)
1.0 (reference)
1.66 (1.09–2.55)
≥ 65 years old
Crude mortality (/1000 person-years)
2.6
2.0
0.8
1.5
HR-Agea (95% CI)
1.0 (reference)
1.09 (0.68–1.74)
1.0 (reference)
1.71 (1.03–2.83)
HR-Allb (95% CI)
1.0 (reference)
1.19 (0.73–1.95)
1.0 (reference)
1.69 (0.99–2.89)
MetS metabolic syndrome, HR hazard ratio, CI confidence interval
aHazard ratios adjusted for age
bHazard ratios adjusted for age, smoking status (never, past, or current smoker), alcohol drinking status (never, past, or current drinker), marital status (yes or no), educational attainment (≤ 15, 16–18, or ≥ 19 years), physical activity (low, middle, high), occupation category (white-collar, blue-collar, or no working), and menopausal status (pre or post) only in women
×
Table 4 shows the predictive effect of each MetS component on cancer mortality. The effects of obesity in women (multivariate-adjusted HR 1.48; 95% CI 1.15–1.91) and elevated PG in both men (multivariate-adjusted HR 1.49; 95% CI 1.18–1.88) and women (multivariate-adjusted HR 1.44; 95% CI 1.03–2.03) on predicting cancer mortality were significantly greater in participants with MetS than in those without the syndrome.
Table 4
Multivariate analysis of cancer mortality with metabolic syndrome according to each metabolic risk factor by sex
Presence
Participants
Cancer deaths
Parson-years
Crude mortality (/1000 person-years)
HR-agea (95% CI)
HR-allb (95% CI)
Men
Obesity
No
3484
378
62,224
6.1
1.00
1.00
Yes
1011
95
18,602
5.1
0.97 (0.77–1.21)
0.99 (0.78–1.26)
Elevated blood pressure
No
2053
191
37,693
5.1
1.00
1.00
Yes
2442
282
43,150
6.5
0.96 (0.80–1.16)
0.99 (0.82–1.21)
Elevated plasma glucose
No
3833
378
69,607
5.4
1.00
1.00
Yes
662
95
11,247
8.4
1.46 (1.17–1.83)
1.49 (1.18–1.88)
Dyslipidemia
No
2676
280
48,221
5.8
1.00
1.00
Yes
1819
193
32,633
5.9
1.15 (0.96–1.38)
1.10 (0.91–1.34)
Women
Obesity
No
5245
195
98,606
2.0
1.00
1.00
Yes
1783
102
33,717
3.0
1.44 (1.14–1.83)
1.48 (1.15–1.91)
Elevated blood pressure
No
3657
127
69,337
1.8
1.00
1.00
Yes
3371
170
63,004
2.7
1.04 (0.82–1.31)
1.02 (0.80–1.31)
Elevated plasma glucose
No
6403
256
120,761
2.1
1.00
1.00
Yes
625
41
11,556
3.5
1.37 (0.98–1.91)
1.44 (1.03–2.03)
Dyslipidemia
No
5143
208
96,688
2.2
1.00
1.00
Yes
1885
89
35,664
2.5
1.03 (0.80–1.31)
1.04 (0.80–1.34)
Obesity: body mass index ≥ 25 kg/m2
Elevated blood pressure: systolic blood pressure and/or diastolic blood pressure ≥ 130/85 mmHg or the use of antihypertensive medication
Elevated plasma glucose: fasting plasma glucose ≥ 6.1 mmol/L (110 mg/dL) (with a fasting duration of at least 3 h) or casual plasma glucose (for less than 3 h or without regard to the time since the last meal) ≥ 7.8 mmol/L (140 mg/dL) and/or the use of antidiabetic medication
Dyslipidemia: triglycerides ≥ 1.69 mmol/L (150 mg/dL) and/or high-density lipoprotein cholesterol < 1.03 mmol/L (40 mg/dL) and/or the use of antihyperlipidemic medication
HR hazard ratio, CI confidence interval
aHazard ratios adjusted for age
bHazard ratios adjusted for age, smoking status (never, past, or current smoker), alcohol drinking status (never, past, or current drinker), marital status (yes or no), educational attainment (≤ 15, 16–18, or ≥ 19 years), physical activity (low, middle, high), occupation category (white-collar, blue-collar, or no working), and menopausal status (pre or post) only in women
Table 5 shows HRs and 95% CIs for cancer-type specific mortality with MetS by sex. The multivariate-adjusted HRs of death from colorectal and breast cancers were 3.48 (95% CI 1.68–7.22) and 11.90 (95% CI 2.25–62.84), respectively, in women. However, no significant difference was observed between MetS and any cancer-type specific mortality in men.
Table 5
Multivariate analysis of cancer-type specific mortality with metabolic syndrome by sex
Presence of MetS
Cancer deaths
Parson-years
Crude mortality (/1000 person-years)
HR-agea (95% CI)
HR-allb (95% CI)
Men
Lung
No
132
2374
1.8
1.00
1.00
Yes
14
256
1.5
0.91 (0.52–1.58)
1.13 (0.65–1.98)
Stomach
No
54
971
0.76
1.00
1.00
Yes
9
162
0.96
1.35 (0.67–2.73)
1.29 (0.58–2.88)
Colon and rectum
No
29
521
0.41
1.00
1.00
Yes
4
72
0.42
1.16 (0.41–3.31)
1.40 (0.48–4.10)
Liver
No
18
324
0.25
1.00
1.00
Yes
4
72
0.42
1.86 (0.63–5.49)
1.57 (0.45–5.53)
Prostate
No
12
216
0.17
1.00
1.00
Yes
2
36
0.21
1.56 (0.35–6.99)
1.41 (0.30–6.61)
Women
Lung
No
30
565
0.25
1.00
1.00
Yes
6
112
0.52
1.82 (0.76–4.37)
1.66 (0.64–4.31)
Stomach
No
42
791
0.35
1.00
1.00
Yes
5
94
0.43
1.09 (0.43–2.76)
0.79 (0.24–2.57)
Colon and rectum
No
30
565
0.25
1.00
1.00
Yes
10
187
0.86
3.07 (1.50–6.27)
3.48 (1.68–7.22)
Liver
No
14
264
0.12
1.00
1.00
Yes
3
56
0.26
1.97 (0.57–6.84)
2.34 (0.66–8.28)
Breast
No
3
57
0.025
1.00
1.00
Yes
3
56
0.26
10.70 (2.11–54.36)
11.90 (2.25–62.84)
MetS metabolic syndrome, HR hazard ratio, CI confidence interval
aHazard ratios adjusted for age
bHazard ratios adjusted for age, smoking status (never, past, or current smoker), alcohol drinking status (never, past, or current drinker), marital status (yes or no), educational attainment (≤ 15, 16–18, or ≥ 19 years), physical activity (low, middle, high), occupation category (white-collar, blue-collar, or no working), and menopausal status (pre or post) only in women
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Sensitivity analyses performed by excluding participants who were younger than 40 years at baseline were consistent with the primary findings. These analyses are described in Additional file 1: Table S1.
Discussion
In the present study, we demonstrated that MetS was associated with increased cancer deaths in women, particularly those younger than 65 years, over a mean follow-up duration of 18.5 years. The predictive value for cancer mortality increased with a higher number of MetS components. The results of the present study are important because the predictive value of MetS for cancer mortality in Japan has not been previously proven.
Only four recent cohort studies have reported a relationship between MetS and cancer mortality [14‐17]. Prospective cohort studies in the U.S. reported that the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) MetS using WC was associated with an increased risk of cancer mortality in men [14], or was not divided by sex [15]. Another prospective cohort study in Korea reported that the NECP-ATP III MetS using BMI instead of WC was associated with increased cancer-related mortality in men, but not in women [16]. The participants in the three cited NCEP-ATP III studies were younger than those in the present study. The number of cancer deaths was small and the prevalence of MetS was also low in the younger generation. In addition, while high estrogen levels may protect against the adverse effects of MetS in young women, MetS and central obesity may affect the risk of cancer in postmenopausal women [24‐26]. However, the results of sensitivity analyses that excluded participants who were younger than 40 years of age at baseline were similar to the primary findings. The Japan Public Health Center-based prospective study (JPHC), which included 34,051 participants (12,412 men and 21,639 women) over a follow-up of 12.3 years, reported that the Japanese MetS using BMI instead of WC was not associated with significantly increased cancer mortality in either sexes [17]. The reason for these JPHC study results may be that this study calculated BMI using a self-administered questionnaire. The current study corroborates these findings and extends them by demonstrating that MetS predicts cancer mortality in women.
The present study also showed that the linear association between increases in the number of MetS components and cancer deaths, and the pathology of obesity is key in MetS because the presence of obesity affected the relationship between the number of MetS components and cancer mortality, whereas the absence of obesity did not. Previous studies also reported a dose–response relationship between MetS components and cancer mortality [14‐16] as well as the risk of cancer [27].
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MetS was positively associated with the risk of colorectal and breast cancer deaths in women. However, the number of cancer-type specific deaths was small. Previous cohort studies reported that MetS was positively associated with cancer mortality in the gastrointestinal system [28], particularly that of colorectal cancer [13, 29]. The Japan Collaborative Cohort Study (JACC) of 96,081 participants (40,510 men and 55,571 women), a nationwide prospective cohort study, reported increased colorectal cancer mortality in women, but not men, with diabetes [30]. A previous meta-analysis reported that MetS was associated with the risk of postmenopausal breast cancer [31].
While many definitions of MetS have been used worldwide, such as the NCEP [32, 33] and International Diabetes Federation (IDF) [34], the original MetS diagnostic criteria were defined in Japan [35]. NCEP and IDF representatives recently agreed that obesity is not an essential item for diagnosis because the clustering of metabolic risk factors is more important than obesity [36]. Therefore, only the Japanese criteria for MetS maintains that obesity is an essential component because it plays a major role in MetS [35]. Although the concept requiring obesity as an indispensable item was based on the pathogenesis of MetS, future studies need to focus on identifying the relationship between cancer and MetS using various criteria (see Additional file 1: Table S2).
There was no significant difference in smoking between participants with and without MetS in both sexes. However, men with MetS smoked more ≥ 21 cigarettes per day than men without MetS (not shown in Table, 18.2% vs. 13.1%, P < 0.001). Current smoker in women may have less impact on MetS than in men because women had lower current smoker than men. There was no significant difference in alcohol drinking between participants with and without MetS in men, while women with MetS were significantly lower alcohol drinkers than women without MetS. One of the reasons may be that light to moderate alcohol consumption decreased the incidence of diabetes [37]. The trends in smoking and alcohol drinking of participants with and without MetS were similar in recent Japanese studies [17, 38, 39].
The mechanisms responsible for the relationship between MetS and an increased risk of cancer death remain unclear; however, potential factors include obesity, insulin resistance, and the insulin-like growth factor (IGF) system [40]. Obesity is associated with inflammation, which leads to insulin resistance [41]. Insulin resistance is a key factor in MetS and increases the risk of cancer mortality [42, 43]. Insulin stimulates the synthesis of IGF-1 and leads to tumor growth [44]. The present study demonstrated that MetS increased cancer deaths in women, but not in men. BMI is a useful indicator of overall adiposity, including visceral adipose tissue (VAT), and VAT is more strongly associated with metabolic risk factors in women than in men [45, 46]. VAT has a direct negative effect on health by releasing a larger amount of excess free fatty acids (FFAs) in women than in men [47]. Triglyceride/FFA cycling is central to the obesity-mediated risk of cancer [48]. Further research is needed to confirm the mechanisms underlying sex-related factors.
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The present study has several potential limitations. The measurement of MetS was based on a single measurement only at baseline, which made it impossible to evaluate the effect of changes in metabolic risk factors over time on cancer mortality. Furthermore, we defined obesity of MetS using BMI instead of WC. Although WC and BMI can produce slight differences in the diagnostic performance and pathological meaning of MetS, BMI ≥ 25 kg/m2 correlates highly well with a WC of ≥ 85 cm in men and ≥ 90 cm in Japan [23]. In addition, owing to the small number of cancer-type specific deaths, there was a possibility of not only beta errors, but also chance.
Conclusion
The present results suggest that MetS is a significant predictor of cancer death in women. Furthermore, there is a dose–response association between an increasing number of MetS components and cancer mortality. These findings implied that subjects with MetS may need to prevention and management of cancer. Further studies are needed to confirm the influence of MetS on the risk of cancer.
Authors’ contributions
JW conducted the statistical analyses and interpretation of data. K. Kayaba, YN and SI contributed data collection and interpretation of the data. JW, EK, K. Kotani and SI conceived and designed the study, and wrote and revised the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We are grateful for the cooperation of all the staff involved in our study.
Competing interests
The authors declare they have no competing interests.
Availability of data and materials
The datasets analyzed in this study are available from the corresponding author (S. Ishikawa, i-shizu@jichi.ac.jp) upon reasonable request.
Consent for publication
Not applicable.
Ethics approval and consent to participate
The present study was approved by the Institutional Review Board of Jichi Medical University, and all participants provided written informed consent at baseline.
Funding
The study was partly supported by a Grant-in-Aid from the Foundation for the Development of the Community, Tochigi, Japan, and Comprehensive Research on Cardiovascular and Life-Style Related Diseases: H26-Junkankitou [Seisaku]-lppan-001.
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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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