Skip to main content

Advertisement

Metabolic syndrome is a risk factor for cancer mortality in the general Japanese population: the Jichi Medical School Cohort Study

Abstract

Background

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.

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 [8,9,10,11], 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 [13,14,15,16,17].

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 [19,20,21]. 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].

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

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
figure1

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

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
figure2

Adjusted hazard curves of cancer mortality with metabolic syndrome by sex

Table 3 Multivariate analysis of cancer mortality with metabolic syndrome by sex

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

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

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 [14,15,16,17]. 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 [24,25,26]. 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 [14,15,16] as well as the risk of cancer [27].

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.

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.

Abbreviations

MetS:

metabolic syndrome

HR:

hazard ratio

CI:

confidence interval

CVD:

cardiovascular diseases

JMS:

Jichi Medical School

BMI:

body mass index

PAI:

physical activity index

SBP:

systolic blood pressure

DBP:

diastolic blood pressure

TC:

serum total cholesterol

HDL-C:

high-density lipoprotein cholesterol

TG:

triglyceride

PG:

plasma glucose

ICD-10:

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

References

  1. 1.

    Meigs JB. Invited commentary: insulin resistance syndrome? Syndrome X? Multiple metabolic syndrome? A syndrome at all? Factor analysis reveals patterns in the fabric of correlated metabolic risk factors. Am J Epidemiol. 2000;152:908–11.

  2. 2.

    Russo A, Autelitano M, Bisanti L. Metabolic syndrome and cancer risk. Eur J Cancer. 2008;44:293–7.

  3. 3.

    Esposito K, Chiodini P, Colao A, Lenzi A, Giugliano D. Metabolic syndrome and risk of cancer: a systematic review and meta-analysis. Diabetes Care. 2012;35:2402–11.

  4. 4.

    Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–86.

  5. 5.

    Statistics and Information Department, Minister’s Secretariat, Ministry of Health, Labour and Welfare. Vital Statistics of Japan; 2016. https://www.mhlw.go.jp/english/. Accessed 19 Oct 2018.

  6. 6.

    Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med. 2003;348:1625–38.

  7. 7.

    Grossman E, Messerli FH, Boyko V, Goldbourt U. Is there an association between hypertension and cancer mortality? Am J Med. 2002;112:479–86.

  8. 8.

    Levine W, Dyer AR, Shekelle RB, Schoenberger JA, Stamler J. Post-load plasma glucose and cancer mortality in middle-aged men and women. 12-year follow-up findings of the Chicago Heart Association Detection Project in Industry. Am J Epidemiol. 1990;131:254–62.

  9. 9.

    Jee SH, Ohrr H, Sull JW, Yun JE, Ji M, Samet JM. Fasting serum glucose level and cancer risk in Korean men and women. JAMA. 2005;293:194–202.

  10. 10.

    Hirakawa Y, Ninomiya T, Mukai N, Doi Y, Hata J, Fukuhara M, et al. Association between glucose tolerance level and cancer death in a general Japanese population: the Hisayama Study. Am J Epidemiol. 2012;176:856–64.

  11. 11.

    Chen Y, Wu F, Saito E, Lin Y, Song M, Luu HN, et al. Association between type 2 diabetes and risk of cancer mortality: a pooled analysis of over 771,000 individuals in the Asia Cohort Consortium. Diabetologia. 2017;60:1022–32.

  12. 12.

    Cowan LD, O’Connell DL, Criqui MH, Barrett-Connor E, Bush TL, Wallace RB. Cancer mortality and lipid and lipoprotein levels. Lipid Research Clinics Program Mortality Follow-up Study. Am J Epidemiol. 1990;131:468–82.

  13. 13.

    Trevisan M, Liu J, Muti P, Misciagna G, Menotti A, Fucci F, et al. Markers of insulin resistance and colorectal cancer mortality. Cancer Epidemiol Biomarkers Prev. 2001;10:937–41.

  14. 14.

    Jaggers JR, Sui X, Hooker SP, LaMonte MJ, Matthews CE, Hand GA, et al. Metabolic syndrome and risk of cancer mortality in men. Eur J Cancer. 2009;45:1831–8.

  15. 15.

    Gathirua-Mwangi WG, Monahan PO, Murage MJ, Zhang J. Metabolic syndrome and total cancer mortality in the Third National Health and Nutrition Examination Survey. Cancer Causes Control. 2017;28:127–36.

  16. 16.

    Lee JS, Cho SI, Park HS. Metabolic syndrome and cancer-related mortality among Korean men and women. Ann Oncol. 2010;21:640–5.

  17. 17.

    Saito I, Iso H, Kokubo Y, Inoue M, Tsugane S. Metabolic syndrome and all-cause and cardiovascular disease mortality: Japan Public Health Center-based Prospective (JPHC) Study. Circ J. 2009;73:878–84.

  18. 18.

    Ishikawa S, Gotoh T, Nago N, Kayaba K, Jichi Medical School (JMS) Cohort Study Group. The Jichi Medical School (JMS) Cohort Study: design, baseline data and standardized mortality ratios. J Epidemiol. 2002;12:408–17.

  19. 19.

    Kannel WB, Sorlie P. Some health benefits of physical activity. Arch Intern Med. 1979;139:857–61.

  20. 20.

    Hayasaka S, Shibata Y, Ishikawa S, Kayaba K, Gotoh T, Noda T, et al. Physical activity and all-cause mortality in Japan: the Jichi Medical School (JMS) Cohort Study. J Epidemiol. 2009;19:24–7.

  21. 21.

    Shibata Y, Hayasaka S, Yamada T, Goto Y, Ojima T, Ishikawa S, et al. Physical activity and cardiovascular disease in Japan: the Jichi Medical School Cohort Study. 2010;20:225–30.

  22. 22.

    Hirokawa K, Tsutsumi A, Kayaba K, Jichi Medical School Cohort group. Occupation and plasma fibrinogen in Japanese male and female workers: the Jichi Medical School Cohort study. Soc Sci Med. 2009;68:1091–7.

  23. 23.

    Examination Committee of Criteria for ‘Obesity Disease’ in Japan, Japan Society for the Study of Obesity. New criteria for ‘obesity disease’ in Japan. Circ J. 2002;66:987–92.

  24. 24.

    Murphy TK, Calle EE, Rodriguez C, Kahn HS, Thun MJ. Body mass index and colon cancer mortality in a large prospective study. Am J Epidemiol. 2000;152:847–54.

  25. 25.

    Healy LA, Ryan AM, Carroll P, Ennis D, Crowley V, Boyle T, et al. Metabolic syndrome, central obesity and insulin resistance are associated with adverse pathological features in postmenopausal breast cancer. Clin Oncol (R Coll Radiol). 2010;22:281–8.

  26. 26.

    Hamid O, Robert C, Ribas A, Hodi S, Walpole E, Daud A, et al. Antitumour activity of pembrolizumab in advanced mucosal melanoma: a post hoc analysis of KEYNOTE-001, 002, 006. Br J Cancer. 2018. https://doi.org/10.1038/s41416-018-0207-6.

  27. 27.

    Shi J, Xiong L, Li J, Cao H, Jiang W, Liu B, et al. A linear dose-response relationship between fasting plasma glucose and colorectal cancer risk: Systematic review and meta-analysis. Sci Rep. 2015;5:17591.

  28. 28.

    Matthews CE, Sui X, LaMonte MJ, Adams AA, Hébert JR, Blair SN. Metabolic syndrome and risk of death from cancers of the digestive system. Metabolism. 2010;59:1231–9.

  29. 29.

    Colangelo LA, Gapstur SM, Gann PH, Dyer AR, Liu K. Colorectal cancer mortality and factors related to the insulin resistance syndrome. Cancer Epidemiol Biomarkers Prev. 2002;11:385–91.

  30. 30.

    Tan C, Mori M, Adachi Y, Wakai K, Suzuki S, Suzuki K, et al. Diabetes mellitus and risk of colorectal cancer mortality in Japan: the Japan Collaborative Cohort Study. Asian Pac J Cancer Prev. 2016;17:4681–8.

  31. 31.

    Esposito K, Chiodini P, Capuano A, Bellastella G, Maiorino MI, Rafaniello C, et al. Metabolic syndrome and postmenopausal breast cancer: systematic review and meta-analysis. Menopause. 2013;20:1301–9.

  32. 32.

    Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA. 2001;285:2486–97.

  33. 33.

    Guerra S, Boscari F, Avogaro A, Di Camillo B, Sparacino G, de Kreutzenberg SV. Hemodynamics assessed via approximate entropy analysis of impedance cardiography time series: effect of metabolic syndrome. Am J Physiol Heart Circ Physiol. 2011;301:H592–8.

  34. 34.

    Alberti KG, Zimmet P, Shaw J, IDF Epidemiology Task Force Consensus Group. The metabolic syndrome—a new worldwide definition. Lancet. 2005;366:1059–62.

  35. 35.

    Committee to Evaluate Diagnostic Standards for Metabolic Syndrome. Definition and the diagnostic standard for metabolic syndrome. Nihon Naika Gakkai Zasshi. 2005;94:794–809 (in Japanese).

  36. 36.

    Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120:1640–5.

  37. 37.

    Polsky S, Akturk HK. Alcohol consumption, diabetes risk, and cardiovascular disease within diabetes. Curr Diab Rep. 2017;17:136.

  38. 38.

    Takashima N, Miura K, Hozawa A, Kadota A, Okamura T, Nakamura Y, et al. Population attributable fraction of smoking and metabolic syndrome on cardiovascular disease mortality in Japan: a 15-year follow up of NIPPON DATA90. BMC Public Health. 2010;10:306.

  39. 39.

    Kokubo Y, Okamura T, Yoshimasa Y, Miyamoto Y, Kawanishi K, Kotani Y, et al. Impact of metabolic syndrome components on the incidence of cardiovascular disease in a general urban Japanese population: the suita study. Hypertens Res. 2008;31:2027–35.

  40. 40.

    Mendonça FM, de Sousa FR, Barbosa AL, Martins SC, Araújo RL, Soares R, et al. Metabolic syndrome and risk of cancer: which link? Metabolism. 2015;64:182–9.

  41. 41.

    Bastard JP, Maachi M, Lagathu C, Kim MJ, Caron M, Vidal H, et al. Recent advances in the relationship between obesity, inflammation, and insulin resistance. Eur Cytokine Netw. 2006;17:4–12.

  42. 42.

    Gallagher EJ, LeRoith D. Obesity and diabetes: the increased risk of cancer and cancer-related mortality. Physiol Rev. 2015;95:727–48.

  43. 43.

    Ben-Shmuel S, Rostoker R, Scheinman EJ, LeRoith D. Metabolic syndrome, type 2 diabetes, and cancer: epidemiology and potential mechanisms. Handb Exp Pharmacol. 2016;233:355–72.

  44. 44.

    Renehan AG, Frystyk J, Flyvbjerg A. Obesity and cancer risk: the role of the insulin-IGF axis. Trends Endocrinol Metab. 2006;17:328–36.

  45. 45.

    Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116:39–48.

  46. 46.

    Tanaka S, Togashi K, Rankinen T, Pérusse L, Leon AS, Rao DC, et al. Sex differences in the relationships of abdominal fat to cardiovascular disease risk among normal-weight white subjects. Int J Obes Relat Metab Disord. 2004;28:320–3.

  47. 47.

    Nielsen S, Guo Z, Johnson CM, Hensrud DD, Jensen MD. Splanchnic lipolysis in human obesity. J Clin Invest. 2004;113:1582–8.

  48. 48.

    Gong Y, Dou LJ, Liang J. Link between obesity and cancer: role of triglyceride/free fatty acid cycling. Eur Rev Med Pharmacol Sci. 2014;18:2808–20.

Download references

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.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Correspondence to Shizukiyo Ishikawa.

Additional file

13098_2018_398_MOESM1_ESM.docx

Additional file 1: Table S1. Multivariate analysis of cancer mortality with metabolic syndrome by sex, excluding participants <40 years. Table S2. Multivariate analysis of cancer mortality with the NCEP-ATP III and IDF by sex. Figure S1. The log-negative-log plot of the survival function for the number of metabolic syndrome components against time to death/follow-up time. Figure S2. The log-negative-log plot of the survival function for participants with and without metabolic syndrome against time to death/follow-up time.

Rights and permissions

Open Access This 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.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Keywords

  • Metabolic syndrome
  • Cohort studies
  • Neoplasm
  • Mortality
  • Japanese