Association between metabolic syndrome and 13 types of cancer in Catalonia: A matched case-control study.

BACKGROUND: Metabolic syndrome (MS) is the simultaneous occurrence of a cluster of predefined cardiovascular risk factors. Although individual MS components are associated with increased risk of cancer, it is still unclear whether the association between MS and cancer differs from the association between individual MS components and cancer. The aim of this matched case-control study was to estimate the association of 13 types of cancer with (1) MS and (2) the diagnosis of 0, 1 or 2 individual MS components.
METHODS: Cases included 183,248 patients ≥40 years from the SIDIAP database with incident cancer diagnosed between January 2008-December 2017. Each case was matched to four controls by inclusion date, sex and age. Adjusted conditional logistic regression models were used to evaluate the association between MS and cancer risk, comparing the effect of global MS versus having one or two individual components of MS.
RESULTS: MS was associated with an increased risk of the following cancers: colorectal (OR: 1.28, 95%CI: 1.23-1.32), liver (OR: 1.93, 95%CI: 1.74-2.14), pancreas (OR: 1.79, 95%CI: 1.63-1.98), post-menopausal breast (OR: 1.10, 95%CI: 1.06-1.15), pre-menopausal endometrial (OR: 2.14, 95%CI: 1.74-2.65), post-menopausal endometrial (OR: 2.46, 95%CI: 2.20-2.74), bladder (OR: 1.41, 95%CI: 1.34-1.48), kidney (OR: 1.84, 95%CI: 1.69-2.00), non-Hodgkin lymphoma (OR: 1.23, 95%CI: 1.10-1.38), leukaemia (OR: 1.42, 95%CI: 1.31-1.54), lung (OR: 1.11, 95%CI: 1.05-1.16) and thyroid (OR: 1.71, 95%CI: 1.50-1.95). Except for prostate, pre-menopause breast cancer and Hodgkin and non-Hodgkin lymphoma, MS is associated with a higher risk of cancer than 1 or 2 individual MS components. Estimates were significantly higher in men than in women for colorectal and lung cancer, and in smokers than in non-smokers for lung cancer.
CONCLUSION: MS is associated with a higher risk of developing 11 types of common cancer, with a positive correlation between number of MS components and risk of cancer.

Introduction

Metabolic Syndrome (MS) is the cluster of cardiovascular risk factors such as obesity (specifically central obesity), hypertension, dyslipidaemia and insulin resistance [1]. MS is a growing public health concern due to its high global prevalence. Studies from the United States indicate that MS increases with age and that it has a total prevalence of 24% in the general population and of 50% in patients with ischemic cardiopathy and other cardiovascular conditions [2]. In Spain, the prevalence also increases with age, it ranges between 23% and 31% in the general population, and it affects more men than women in people under 65 years of age [3, 4].

MS was initially considered a risk factor just for cardiovascular disease [5]. However, some studies [6-9] associate MS with a higher risk of liver, colorectal and bladder cancer in men; and endometrial, pancreatic, colorectal, ovarian and post-menopausal breast cancer in women. The results from studies on prostate cancer and MS are inconclusive, while some of them show an increase in risk [10], others show a reduction [5]. A published meta-analysis also found a higher risk of haematological cancer in patients with MS [11].

Some studies show that 1 or 2 components of MS are individually associated with colorectal, breast, endometrial, bladder, kidney, lung and thyroid cancer [9, 12–15]. Specifically, the effect of obesity and diabetes on the incidence of colorectal, pancreatic, liver, kidney, breast and endometrial cancer has already been described [16, 17]. However, no evidence has been yet provided for the impact of MS components in other less common cancers [11]. Large population studies are needed to elucidate if the risk of MS on cancer is higher than the risk associated with each MS component.

The main aim of this study was to investigate the association between MS and 13 types of cancer in Catalonia, using data collected from 2006–2017 in a large electronic health records validated database [18, 19]. We also aimed to evaluate the association of one or two MS components with cancer risk.

Material and methods

Data source and setting

We conducted a matched case-control study using the Information System for Research in Primary Care (SIDIAP; www.sidiap.org) [18]. This database comprises the electronic health records of 286 primary healthcare centres (6 million of patients, 80% of residents of Catalonia, Spain). The SIDIAP includes sociodemographic data, clinical diagnoses (using the International Classification of Diseases (ICD-10)), clinical variables, referrals, laboratory tests results and medication invoices (using the Anatomical Therapeutic Chemical (ATC) Classification System).

Study population

All individuals ≥ 40 years of age with information in the SIDIAP database between 01/01/2006 and 31/12/2017 were suitable to be included. Patients were excluded from participation when they presented with secondary cancers and metastases.

A total of 190,505 individuals with incident cancer were included. Of these, we later excluded 334 men with breast cancer, 6,826 cases because they were diagnosed with more than one cancer on the same day, and 61 because they were >99 years of age on the index date. Finally, a total of 183,284 cases and 733,136 paired controls, four controls for each case, were included (Fig 1).

Fig 1

Study flow chart.

Cancer definition

Cancer cases were defined as individuals with an incident diagnosis of selected types of cancer between 01/01/2008 and 31/12/2017. We decided to include the most frequent cancer types (ICD-10 codes) in Spain as outcomes. Even though there is evidence of the association between MS and some of these cancer types such as colorectal (C18+20), prostate (C61), liver (C22), bladder (C67), endometrium (C54), pancreas (C25) and breast (C50). Prior studies have not investigated the MS-cancer association for several cancer types using a systematic analysis approach like lung cancer (C34) and kidney cancer (C64) but were included due to their high prevalence in the general population. In addition, we included some less frequently occurring cancer types such as thyroid (C73), Hodgkin lymphoma (C81), non- Hodgkin lymphoma (C82-85) and leukemia (C91-95), for which the current literature is limited.

An association between MS and more cancer types than currently recognized in the literature is possible given that the components of MS can trigger biological (hormonal, inflammation, and oxidative stress) processes involved in tumor development.

Breast and endometrial cancers were categorized into pre- and post-menopausal because of the well-established evidence indicating a different impact of obesity and estrogens on these two stages of life [20]. The date of the cancer diagnosis was considered as the index date for cases.

Cancer diagnoses in the SIDIAP are validated against population-based cancer registries [19].

Control definition

Four controls obtained from the source population were selected for each case, considering as index date of the control the same date of the selection of the case. Each paired case-control was of the same sex and age (± 1 year). No more controls were obtained as it has been previously shown that little statistical power is gained by further increasing this ratio [21].

MS definition

According to the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) criteria, a patient is diagnosed with MS when they present with 3 or more of the following variables: Obesity, High Blood Pressure (HBP), reduced HDL cholesterol, elevated Triglycerides and high Glycemia [1].

Obesity is defined as a body mass index (BMI)> 30 kg/m2, an indicator of overall adiposity. Although central adiposity (usually measured with waist circumference (WC)) is preferred to define this component, we used the BMI in agreement with the WHO definition of MS, since WC was unavailable for most patients in the SIDIAP database [22].

Details for MS construction are published elsewhere [23].

When an abnormal value of any MS component was identified in the database it was assessed the association between cancer and one MS component. If a second component was identified, the association between cancer and two MS component was considered, independently of the time elapsed between the first and the second component identified. When a third component was identified, it was considered that the patient had ≥3 components (and diagnosed with MS). In some patients, more than one measure was recorded on the same day; we considered the average of these values.

Following these definitions, a composite variable of 0, 1, 2, ≥3 MS components was constructed. Both cases and controls had to be exposed either to MS or to 1 or 2 MS components for at least 2 years before the index date (cancer diagnosis or control identification) to avoid reverse causality.

Covariables

We also extracted information (2 years before the index date) on age; sex (women, men); nationality (Spanish, non-Spanish); the MEDEA deprivation index (census tract-based deprivation index to identify socioeconomic status in urban areas) categorized in quintiles and rural area; smoking status (non-smokers, ex-smokers and current smokers); alcohol intake calculated in standard units (no alcohol, low and high consumption); dispensation of drugs such as hormonal replacement therapy among menopausal women, paracetamol, aspirin and ibuprofen (classified as yes/ no); presence of hepatitis (classified as yes/ no) and menopause (classified as yes/ no). Women without information on menopausal status ≥ 50 years of age at least two years before the index date were considered to be menopausal.

Statistical analysis

An initial descriptive analysis of the included population was performed using mean (standard deviation) and median (interquartile range) for quantitative variables and percentages for categorical variables. To assess differences between cases and controls, the t-test or the U Mann-Whitney test for quantitative variables and the Chi-squared test for qualitative variables were performed.

We conducted a conditional logistic regression model to evaluate the association between MS and cancer risk, comparing the effect of global MS versus the individual components of MS, and controlling for the following potential confounders: age, MEDEA Deprivation Index, smoking status and nationality. Hepatitis and other liver diseases were included as confounders in the liver cancer analysis.

All analyses were stratified by type of cancer. Additionally, interaction analyses were performed to explore if the association between MS and cancer differed according to sex and smoking status. To address potential biases due to variables with missing information, multiple imputation by chained equations with 20 imputed datasets was applied to covariates [24-26]. Estimates from each imputed dataset were combined following the rules outlined by Rubin [27].

To assess potential exposure misclassification due the use of BMI instead of WC, we also conducted a sensitivity analysis including only people with at least one WC measurement in the database (WC ≥102 cm in men and ≥88 cm in women are considered central obesity indicators).

Further sensitivity analyses considered two measures of each component separated at least by 2 weeks (maximum 1 year) to ensure that the patient had that component of MS.

The level of statistical significance was 0.05. All analyses were carried out with the statistical packages SPSS 24 (SPSS Inc., Chicago, IL, USA) and Stata 15 (StataCorp LLC., College Station, Texas, USA).

Ethics approval and consent to participate

This study follows all national and international regulations: Declaration of Helsinki and Principles of Good Research Practice.

In accordance with European and Spanish legislation on confidentiality and data protection ([EU] 2016/679), the data contained in SIDIAP are always pseudonymised. Thus, it is not necessary to ask for informed consent from the participants and so was waived by the Clinical Ethics Committee at IDIAPJGol.

For the link with the CMBD database, SIDIAP uses a third party to ensure confidentiality. The study protocol was approved by the Clinical Research Ethics Committee of IDIAPJGol (P17/212) on November 29, 2017. Anonymity and confidentiality of data and medical records were guaranteed at all times in accordance with the Organic Law 15/1999 on the Protection of Personal Data (http://www.boe.es/boe/dias/1999/12/14/pdfs/A43088-43099.pdf).

Results

The distribution of cancer in the 183,284 cases was as follows: 36,204 colorectal; 5,754 liver; 5,417 pancreas; 37,647 breast (13,572 pre-menopausal breast and 24,075 post-menopausal breast); 5,386 endometrial (1,124 pre-menopausal endometrial and 4,262 post-menopausal endometrial); 20,799 bladder; 6,833 kidney; 30,888 prostate; 682 Hodgkin lymphoma; 3,621 non-Hodgkin lymphoma; 6,957 leukaemia; 20,387 lung and 2,709 thyroid (Table 1). Four controls for each case (733,136 in total) were selected. Fig 1 shows the flow chart of the selection process of the study participants.

Table 1

Association between selected cancers and number of Metabolic syndrome components.

	Metabolic Syndrome n(%)
	0 components	1 component	2 components	MS(≥3 components)
N total	253661	236777	175968	250014
Digestive
Colorectal Cancer	7957 (22.0)	9157 (25.3)	7394 (20.4)	11696 (32.3)
Controls	35959 (24.8)	37848 (26.1)	28849 (19.9)	42160 (29.1)
Liver Cancer	1067 (18.5)	1483 (25.8)	1239 (21.5)	1965 (34.2)
Controls	5994 (26.0)	5967 (25.9)	4477 (19.5)	6578 (28.6)
Pancreas Cancer	996 (18.4)	1257 (23.2)	1168 (21.6)	1996 (36.8)
Controls	5252 (24.2)	5488 (25.3)	4387 (20.2)	6541 (30.2)
Gynecological
Pre-Menopause Breast Cancer	8251 (60.8)	3130 (23.1)	1243 (9.2)	948 (7.0)
Pre-Menopause Controls	32397 (60.1)	11798 (21.9)	5249 (9.7)	4480 (8.3)
Post-Menopause Breast Cancer	5552 (23.1)	6655 (27.6)	4672 (19.4)	7196 (29.9)
Post-Menopause Controls	23359 (24.2)	26665 (27.6)	18624 (19.3)	28016 (29.0)
Pre-Menopause Endometrial Cancer	515 (45.8)	272 (24.2)	157 (14.0)	180 (16.0)
Pre-Menopause Controls	2530 (56.7)	1045 (23.4)	466 (10.4)	422 (9.5)
Post-Menopause Endometrial Cancer	647 (15.2)	942 (22.1)	842 (19.8)	1831 (43.0)
Post-Menopause Controls	4074 (23.9)	4512 (26.4)	3362 (19.7)	5133 (30.1)
Urological
Bladder Cancer	4152 (20.0)	5281 (25.4)	4484 (21.6)	6882 (33.1)
Controls	20233 (24.3)	21474 (25.8)	17261 (20.7)	24228 (29.1)
Kidney Cancer	1517 (22.2)	1756 (25.7)	1298 (19.0)	2262 (33.1)
Controls	8012 (29.3)	6848 (25.1)	5138 (18.8)	7334 (26.8)
Prostate Cancer	6725 (21.8)	8836 (28.6)	6940 (22.5)	8387 (27.2)
Controls	29141 (23.6)	32839 (26.6)	26110 (21.1)	35462 (28.7)
Hematological
Hodgkin Lymphoma	291 (42.7)	141 (20.7)	116 (17.0)	134 (19.6)
Controls	1220 (44.7)	642 (23.5)	379 (13.9)	487 (17.9)
Non-Hodgkin Lymphoma	1115 (30.8)	909 (25.1)	677 (18.7)	920 (25.4)
Controls	4895 (33.8)	3624 (25.0)	2482 (17.1)	3483 (24.0)
Leukaemia	1532 (22.0)	1788 (25.7)	1443 (20.7)	2194 (31.5)
Controls	7334 (26.4)	7157 (25.7)	5426 (19.5)	7911 (28.4)
Others
Lung Cancer	5005 (24.5)	5303 (26.0)	4050 (19.9)	6029 (29.6)
Controls	22142 (27.2)	20694 (25.4)	16031 (19.7)	22681 (27.8)
Thyroid Cancer	1002 (37.0)	663 (24.5)	461 (17.0)	583 (21.5)
Controls	4795 (44.3)	2603 (24.0)	1543 (14.2)	1895 (17.5)

Baseline characteristics of cases and controls are summarized in Table 2. The mean age of cases and controls was 67.5 years (SD 12.4). Women accounted for 56.3% of study participants. Table 1 shows the distribution of the composite variable related to the number of MS components by different types of cancer. An association was observed between the number of MS components and all cancers studied, except for pre-menopausal breast cancer, prostate cancer and Hodgkin Lymphoma. MS prevalence was higher in cases than in controls except for pre-menopausal breast cancer (7.0% vs. 8.3%) and prostate cancer (27.2% vs. 28.4%). The cancer with the highest prevalence of MS was post-menopausal endometrial cancer (43.0% in cases compared to 30.1% in matched controls).

Table 2

Characteristics of cancer cases and matched controls.

	All Cases n(%)	All Controls n(%)
N total	183284	733136
Age mean (SD)	67.5 (12.4)	67.5 (12.4)
Median (IQR)	68 (58–77)	68 (58–77)
Metabolic Syndrome
No component	46324 (25.3)	207337 (28.3)
1 component	47573 (26.0)	189204 (25.8)
2 components	36184 (19.7)	139784 (19.1)
MS	53203 (29.0)	196811 (26.8)
Sex
Men	80051 (43.7)	320204 (43.7)
Women	103233 (56.3)	412932 (56.3)
Nationality
Spanish	178241 (97.2)	691888 (94.4)
Non-Spanish	5043 (2.8)	41248 (5.6)
MEDEA index
Quintile 1	29788 (16.3)	117426 (16.0)
Quintile 2	26394 (14.4)	104654 (14.3)
Quintile 3	25126 (13.7)	102756 (14.0)
Quintile 4	23655 (12.9)	100601 (13.7)
Quintile 5	20359 (11.1)	88566 (12.1)
Rural	33951 (18.5)	138931 (19.0)
Missings	24011 (13.1)	80202 (10.9)
Smoking status
Never smoker	58529 (31.9)	248919 (34.0)
Ex-smoker	20881 (11.4)	75228 (10.3)
Smoker	19780 (10.8)	62682 (8.5)
Missings	84094 (45.9)	346307 (47.2)
Alcohol intake
No consumption	58923 (32.1)	233338 (31.8)
Low consumption	34357 (18.7)	127687 (17.4)
High consumption	3561 (1.9)	10967 (1.5)
Missings	86443 (47.2)	361144 (49.3)
Hormonal therapy (women postmenopausia)
No consumption	54999 (93.4)	220858 (93.5)
Consumption	3916 (6.6)	15250 (6.5)
Paracetamol
No consumption	129196 (70.5)	530011 (72.3)
Consumption	54088 (29.5)	203125 (27.7)
Acetylsalicylic acid (ASA)
No consumption	151326 (82.6)	610683 (83.3)
Consumption	31958 (17.4)	122453 (16.7)
Ibuprofen
No consumption	156708 (85.5)	633580 (86.4)
Consumption	26576 (14.5)	99556 (13.6)
Chronic Hepatitis
No hepatitis	181927 (99.3)	724036 (98.8)
Hepatitis B	258 (0.1)	2114 (0.3)
Hepatitis C	1078 (0.6)	6853 (0.9)
Other/unspecified hepatitis	21 (0)	133 (0)
Menarche age mean (SD)	12.6 (1.6)	12.7 (1.6)
Median (IQR)	13 (12–14)	13 (12–14)
Missings n(%)	63746 (79.6)	258683 (80.8)
Menopause
No	21136 (26.4)	84096 (26.3)
Yes	58915 (73.6)	236108 (73.7)
Primary care visits between 2 and 4 years before data index mean (SD)	14.2 (17.4)	13.5 (17.6)
Median (IQR)	9 (1–21)	8 (0–20)

SD, Standard Deviation; IQR, Inter Quartile Range, MS, Metabolic Syndrome.

Hypertension was the most frequent component of MS among the patients included in the study with exposure to only one component (80.3 and 80.4 cases and controls, respectively). In patients exposed to two components, the most frequent combination was hypertension + high glycemia (47.1 and 46.3 cases and controls, respectively). Lastly, in patients exposed to ≥3 components, the most frequent combination was hypertension + high glycemia + obesity (17.5 and 17.7 cases and controls, respectively) (S1 Table).

Regarding controls, more MS components (gradient from 0 to ≥ 3) were observed in women, older patients, participants living in deprived areas, smokers and patients with a lower registered consumption of paracetamol, ASA and ibuprofen (S2 Table).

The cancer types associated with MS in the adjusted models were post-menopausal endometrial (OR 2.46, 95%CI 2.20–2.74), pre-menopausal endometrial (OR 2.14, 95%CI 1.74–2.65), liver (OR 1.93, 95%CI 1.74–2.14), kidney (OR 1.84, 95%CI 1.69–2.00), pancreas (OR 1.79, 95%CI 1.63–1.98), thyroid (OR 1.71, 95%CI 1.50–1.85), leukaemia (OR 1.42, 95%CI 1.31–1.54), bladder (OR 1.41, 95%CI 1.34–1.48), colorectal (OR 1.28, 95%CI 1.23–1.32), non-Hodgkin lymphoma (OR 1.23, 95%CI 1.10–1.38), lung (OR 1.11, 95%CI 1.05–1.16) and post-menopausal breast (OR 1.10, 95%CI 1.06–1.15). No association was found between MS and Hodgkin lymphoma (OR 1.19, 95%CI 0.78–1.82). The ORs in gynaecological cancers were higher in post-menopausal (OR: 1.10 95%CI: 1.06–1.15 and OR: 2.46 95%CI: 2.20–2.75 for breast and endometrial cancer, respectively) than pre-menopausal women (OR: 0.85, 95%CI: 0.78–0.92 and OR: 2.14 95%CI: 1.74–2.65 for breast and endometrial cancer, respectively) (Fig 2).

Fig 2

Adjusted ORs and 95% confidence intervals according to metabolic syndrome by selected cancers.

ORs are presented by squares, with their 95% CIs as horizontal lines; OR, odds ratio; CI, confidence interval. Reference category is 0 components. All models are adjusted by age, MEDEA Deprivation Index, smoking status and nationality. *Also adjusted by hepatitis and others liver diseases. Multiple imputation by chained equations with 20 imputed datasets were applied to outcomes and covariates.

The increasing number of MS components positively correlates with cancer risk in adjusted models, except for prostate, lung, pre-menopausal breast cancer and non-Hodgkin lymphoma. With the increasing number of MS components, the protective power on pre-menopausal breast cancer increase. Interestingly, while MS was not associated with increased risk of prostate cancer, there was a correlation between the presence of 1 or 2 components of MS and the risk of this cancer (OR 1.15, 95%CI 1.11–1.19 and OR 1.14, 95%CI 1.10–1.19 for 1 and 2 components, respectively). In contrast, the risk of lung cancer was similar for participants with 1, 2 and ≥ 3 (MS) components (OR 1.09, 95%CI 1.05–1.15 and OR 1.08. 95%CI 1.02–1.13 for 1 and 2 components and OR 1.11. 95%CI 1.05–1.16 for MS). Participants with 1 or 2 components presented a higher risk of pre-menopausal breast cancer than participants with MS (OR 1.03, 95%CI 0.98–1.08 and OR 0.94, 95%CI 0.88–1.01 for 1 and 2 components and OR 0.85, 95%CI 0.78–0.92 for MS). Participants with 2 components presented a similar risk of non-Hodgkin lymphoma than participants with MS (Fig 2).

We stratified all MS-cancer associations by sex (Fig 3). For colorectal and lung cancer, the risk of MS was higher in men (OR: 1.33 95%CI 1.27–1.40 and OR: 1.14 95%CI 1.08–2.20, respectively) than in women (OR: 1.20 95%CI: 1.14–1.27 and OR: 1.01 95%CI: 0.90–1.12, respectively). The p-values for the interaction between sex and MS were 0.004 and 0.002 for colorectal and lung cancer, respectively. Stratification by sex did not show further differences in the association between MS and cancer.

Fig 3

Adjusted ORs and 95% confidence intervals according to metabolic syndrome by selected cancers and sex.

ORs are presented by squares (in men) and circles (in women), with their 95% confidence intervals as horizontal lines; OR, odds ratio. Reference category is 0 components. All models are adjusted by age, MEDEA Deprivation Index, smoking status and nationality. *Also adjusted by hepatitis and others liver diseases. Multiple imputation by chained equations with 20 imputed datasets were applied to outcomes and covariates.

The association between MS and lung cancer changed when the analysis was stratified according to smoking status (interaction term p value <0.001); the risk in smokers and ex-smokers was higher than in non-smokers (OR: 1.34 95%CI: 1.18–1.52 in smokers, OR: 1.19 95%CI 1.04–1.35 in ex-smokers and OR: 0.93 95%CI: 0.85–1.01 in non-smokers). (Table 3).

Table 3

Adjusted ORs of metabolic syndrome and lung cancer according to tobacco consumption.

	Non-smoker			Ex-smoker			Smoker
	OR	95% CI	P-value	OR	95% CI	P-value	OR	95% CI	P-value
Lung cancer in general
No components	1.00		0.0991	1.00		0.0331	1.00		<0.0011
1 component	0.97	0.90–1.05	0.0422	1.16	1.02–1.31	0.0262	1.25	1.11–1.41	<0.0012
2 components	0.91	0.84–1.00		1.15	1.01–1.32		1.31	1.15–1.50
MS	0.93	0.85–1.01		1.19	1.04–1.35		1.34	1.18–1.52

OR, odds ratio; CI, confidence interval.

Models adjusted by age, medea, alcohol and nationality

1Wald test.

2P-Trend.

Multiple imputation by chained equations with 20 imputed datasets were applied to outcomes and covariates.

We performed two sensitivity analyses in which we altered the main definition of MS.

In the first analysis, two abnormal measures were used to ensure that the patient was exposed to that component. In a second analysis, we used WC instead of BMI to report obesity. The results in sensitive analyses using two measures to define MS components were similar for almost all cancers. However, for liver, kidney and Hodgkin lymphoma the ORs in the models with two measures were slightly higher than the ORs of the main models. The largest difference was found for kidney cancer (OR: 1.84 95%CI 1.69–2.00 vs. OR: 2.23 95%CI 1.95–2.60, in one and two measures, respectively). The ORs were similar when WC was used instead of BMI, except for prostate and lung cancer, although the sample was small due to the high number of missing values. In the main analysis, MS was not associated with prostate cancer. However, when using WC instead of BMI, MS was inversely associated with prostate cancer (OR: 1.02 95%CI 0.98–1.06 vs. OR: 0.70 95%CI 0.55–0.90, respectively). In contrast, in the main analysis MS was a risk factor for lung cancer and when using WC, MS was not associated with lung cancer (OR: 1.11 95%CI 1.05–1.16 vs. OR: 1.01 95%CI 0.70–1.450, respectively (S3 Table)). The Kappa concordance index between BMI and WC was 0.492.

Discussion

In this large population-based study, MS was associated with an increased risk of 11 out of 13 cancers, namely endometrial, liver, kidney, pancreas, leukaemia, bladder, colorectal, non–Hodgkin lymphoma, lung and post-menopausal breast, although the effects differed substantially by cancer type. An increasing number of MS components positively correlated with a significant increase in cancer risk in adjusted models, except for non-Hodgkin lymphoma and prostate, lung and pre-menopausal breast cancer. The observed effect sizes for the cancers associated with MS in our data were broadly consistent with previous studies [7, 9, 13–15, 28–34]. Contrary to our study, Park et al. reported a weaker association between MS and thyroid cancer [14], while Almquist and colleagues failed to report any association [35] using a z-score (standard score) calculation that included all 5 components of MS. In agreement with other studies, our results do not show an association between MS and prostate cancer or Hodgkin lymphoma [36]. The results from studies on prostate cancer and MS are inconclusive; some studies report a reduced risk [5, 12] and others report an increased risk [10, 37] while our study found no significant risk of prostate cancer associated with MS. These discrepancies might be explained by Hammarsten’s hypothesis [38] that MS inversely correlates with localised prostate cancer and positively with advanced disease. Furthermore, a study of Gomez-Gomez et al., showed that each of the individual criterion of MS, circulating testosterone levels and inflammatory status may have on the risk and aggressiveness of prostate cancer [10]. In the case of gynaecological cancers, menopausal status was a determinant factor, especially in breast cancer. In agreement with previous studies, we observed that MS increased breast cancer risk in post-menopausal women, and decreased it in pre-menopausal women [7, 29]. Previous investigations proposed that each component of the metabolic syndrome is connected with systemic alterations. Concerning breast cancer, it has been proposed that components of MS, especially obesity, play different roles in cancer risk according to menopausal status and estrogen receptor status [39]. Obesity is associated with decreased risk of estrogen receptor–positive breast cancer in premenopausal women, but it is closely related with increased risk of estrogen receptor–positive breast cancer in postmenopausal women [7, 29, 40].

The risk of most cancers was higher in individuals with MS than in patients with one or two components of MS. In agreement with the literature [9, 13–15, 28, 31, 34], a positive correlation between MS components and risk of cancer was found in eight of these eleven cancers (colorectal, liver, pancreas, post-menopausal breast, pre- and post-menopausal endometrial, bladder, leukaemia, and thyroid).

Mechanisms that link metabolic syndrome and cancer risk are not fully understood. Metabolic syndrome may be a surrogate marker for other cancer risk factors, such as decreased physical activity, consumption of high–calorie dense foods, high dietary fat intake, low fiber intake, and oxidative stress [7].

In accordance with previous studies, when stratifying by gender, the risk of colorectal, lung and bladder cancer was higher in men [7, 9, 15, 30]. The positive association between MS and lung cancer was greater in smokers, corroborating the results reported in a recent cohort study [15].

While MS has multiple definitions, the most widely recognised criteria to construct MS belong to the NCEP ATP III and the IDF [1]. A study by Qiao et al. found similar results when comparing NCEP ATP III and IDF criteria for the association between MS and lung cancer [41, 42]. In contrast, Xiang and colleagues found that MS was a risk factor for breast cancer following IDF criteria, whereas no statistical association could be established using NCEP ATP III criteria [39].

We also conducted a sensitivity analysis, in which we required two abnormal measurements of each component for diagnosis, and found that it did not significantly affect the results. When using WC in the analysis instead of BMI, the ORs obtained were similar except for lung and prostate cancer. However, the Kappa index between both measurement methods was low. Since we only had WC measurements for 15% of the population and the number of missing values of this variable was too high, we used BMI criteria for obesity in agreement with other publications [5, 31, 34]. Furthermore, when Montella et al. performed similar sensitivity analyses, their results did not significantly change [9], and Gomez-Gomez et al. found a strong correlation between BMI and WC [10].

Our study has several strengths. Firstly, we used a large data source with sufficient statistical power to investigate associations of less frequent cancers (i.e. Hodgkin and non-Hodgkin lymphoma and thyroid). Consequently, we have been able to present the first results on the association between MS and haematological malignancies. SIDIAP patients broadly represent the wider population, suggesting good generalizability to the Catalonian and similar populations. In 2019, Recalde et al. [19] validated the diagnosis of cancer in the SIDIAP and the result was that the SIDIAP includes 76% of cancers recorded in the cancer registries [43-45].

Our study also has limitations. Firstly, we assumed that once a person had an abnormal result, this person was constantly exposed to this component even if later results showed improvement. Some evidence points at the concept of metabolic memory, i.e., even when an individual stops meeting MS criteria, they are still at higher risk of specific cancers (i.e. kidney cancer) [13]. Also, the lack of data on other possible confounders influencing the relationship between MS and cancer, such as physical activity and parity, or information related to previous treatment for other health conditions might have biased the results. In addition, it is necessary to explore the potential association of specific MS criteria and risk of specific cancer types in future studies. This is a case-control study, when estimating the association between MS and cancer; however, we were not able to estimate cumulative incidences or other types of absolute risks, which would have been useful to put the relative increase in absolute terms. The tobacco and alcohol variables have a high percentage of missing values (45.9% and 47.2% for tobacco and alcohol, respectively). This is a significant limitation to our study which we have attempted to mitigate through multiple imputation. While it’s true that multiple imputation has its own set of biases, current theory suggests that the multiple imputation bias is smaller than the analysis with completed-cases. Considering only the complete-cases of the database would result in a smaller sample size, loss of statistical power and theoretically with more bias [46].

The increasing prevalence of MS worldwide and the high incidence of some cancers suggests that a large number of cancer cases diagnosed every year are related to the metabolic syndrome. There is a compelling need for evidence on whether effective interventions to reduce the prevalence of metabolic syndrome in adult populations could reduce cancer risk. The formulation of public health strategies based on lifestyle changes could obtain significant results in the fight against cancer. Investigating the role of MS as a risk factor of specific cancers is crucial to diagnose and treat cancer in earlier stages.

In summary, MS is associated with a higher risk of developing at least 11 cancer types. The risk of most cancers increased with the number of MS components present in an individual. Our results indicate that prevention strategies targeting individual components of MS could reduce the risk of several cancer types.

MS components combination with cancer cases and matched controls.

(DOCX)

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Characteristics of the controls according to metabolic syndrome.

(DOCX)

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Analysis of those patients exposed to a single value of the pathological component versus those who present 2 measures and analysis considering waist circumference instead of BMI.

Adjusted ORs and 95% CI. Multiple imputation by chained equations with 20 imputed datasets were applied to outcomes and covariates. Models adjusted by age, medea, tobacco, alcohol, nationality. aConsider two measures of parameters separated at least by 2 weeks (maximum 1 year) to ensure that the patient has that pathological component of MS. bUsing Waist circumference instead of one measure of BMI. There were 157,872 (86.1%) missing values in the case group and 636,984 (86.9%) missing values in the control group. cAlso adjusted by hepatitis and others liver disease. dThe model does not converge when using Waist circumference instead of one measure od BMI, because the number of observations in this model are very low. 1Wald test. 2P-Trend. OR, odds ratio; CI, confidence interval.

(DOCX)

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7 Dec 2021

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Reviewer #2: Yes

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Reviewer #1: This article reported results MS is statistically associated with a higher risk of developing at 11 cancer types after the evaluation of 13. Moreover, they demonstrated in this manuscript that the risk of most cancers increased with the number of MS components present in an individual. Hence, the authors finally suggest that prevention strategies targeting individual components of MS could reduce the risk of several cancer types. Although the manuscript is potentially interesting, it is mostly descriptive and most of the conclusions have been published previously (see comments below).

Thus, there are major aspects that could be revised to improve the results:

- The authors should take care in the use of “recent” when they are referring to other published works since I cannot consider a recent work that has been published more than 5 years ago. An example is given, “MS was initially considered a risk factor just for cardiovascular disease. However, recent studies associate MS with a higher risk of liver, colorectal, and bladder cancer in men; and endometrial, pancreatic, colorectal, ovarian, and postmenopausal breast cancer in women”. However, the original manuscript was published in 2012(doi: 10.2337/dc12-0336).

- The authors did not give too many reasons for the selection of these 13 cancer types. The authors only said, “However, no evidence has been yet provided for the impact of MS components in other less common cancers”. Conversely, for example, lung cancer has related to some metabolic factors of metabolic syndrome. Please, I am really encouraged to provide more reasons for the selection of these cancer types.

- The author must justify the fact of comparing every case with four controls since a major number of control cases could imply a major statistical power being really important for the final results and conclusions of this study.

-In general, the manuscript needs to get a significant improvement in the discussion section. For instance, the sentence is too descriptive: “In gynecological cancers, the menopausal status was a determinant factor, especially in breast cancer. In agreement with previous studies, we observed that MS increased breast cancer risk in postmenopausal women and decreased it in premenopausal women”.

- Concerning the important limitation of not following up with the patients, the authors should clearly explain this limitation and how it could affect the results of the paper. Furthermore, the authors should provide information about when the MS component was identified such as previous to tumor diagnose (1 year or 2 years), during tumor treatment…

Minor comments:

- A sentence in the abstract is not very clear. “Adjusted conditional logistic regression models were used to estimate OR and 95% CI for the association between individual components of MS and cancer, and MS and cancer.” It seems to be confusing.

- The use of “approximately” when you are talking about the patients available in the study is not accurate for scientific work.

- The authors described in methodology “All individuals ≥ 40-99 years of age with information in the SIDIAP database between 01/01/2006 and 31/12/2017 were included. Final participants included patients with any of the 13 types of incident cancers of interest, together with their paired controls.” Please, the authors should provide the number of patients after the application of each inclusion criteria in the methodology section.

- Typing errors like “MS component..” should be corrected in the new manuscript.

Reviewer #2: In this study, Diana Puente et al studied the potential association between Metabolic syndrome (and number of components met) and the risk of 13 types of cancer. The authors included 183,248 patients from the Information System for Research in Primary Care. The data derived from this study showed that Metabolic Syndrome is associated to 11 cancer types (i.e., endometrial, liver, kidney, pancreas, thyroid, leukaemia, bladder, colorectal, non-Hodgkin lymphoma, lung and post-menopausal breast). These results are interesting for the field and shed light on the relation between Metabolic Syndrome and cancer risk. Therefore, the Reviewer consider that this article is well written, adds to the field valuable information and is suitable to be published in PLOS One. However, the following minor comments should be addressed:

- The manuscript is well-written. However, some typos can be found in the text and should be corrected:

o Remove intro in line 130.

o Remove intro in line 131.

o Two period symbols in line 133.

- In the introduction there is no information about the inconclusive relation between prostate cancer and metabolic syndrome. Authors should include some information about that in the introduction.

- In addition to the presence of Metabolic Syndrome, the authors also analysed the impact of the number of criteria met on cancer risk. However, no information was shared with regards to the potential association of specific MS criteria and cancer risk. As an example, Gomez-Gomez et al showed that high-blood pressure was associated with clinically significant prostate cancer in their cohort of patients (1). Authors should analyse this or add some information about this in the limitations paragraph.

- The authors performed two sensitivity analyses in which they altered the main definition of Metabolyc Sindrome. The data obtained when using WC instead of BMI are especially interested as WC is a better criterion to ‘measure’ obesity. Although the authors appreciated that the sample is smaller when classifying the patients using WC due to the high number of missing values, this information (number of patients/missing values) is not showed in Supplemental Table 3. Therefore, authors should include this information in the final version of the manuscript.

- The fact that patients with 1 or 2 MS components presented a higher risk of pre-menopausal breast cancer and prostate cancer than participants with MS has not been discussed in the manuscript. Could the authors explain this rare phenomenon? The authors should address this question in the discussion section of the final manuscript.

References

1. Gomez-Gomez E, Carrasco-Valiente J, Campos-Hernandez JP, Blanca-Pedregosa AM, Jimenez-Vacas JM, Ruiz-Garcia J, Valero-Rosa J, Luque RM, Requena-Tapia MJ. Clinical association of metabolic syndrome, C-reactive protein and testosterone levels with clinically significant prostate cancer. J Cell Mol Med. 2019;23(2):934-942.

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Reviewer #1: Yes: Fuentes-Fayos, Antonio C

Reviewer #2: No

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Submitted filename: PONE-D-21-30858_reviewer.docx

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31 Jan 2022

Dear editor,

Many thanks for allowing us the opportunity to revise and resubmit this manuscript. The reviewers have provided us with many constructive suggestions. We have addressed each reviewer’s point below. Two versions of the revised manuscript have been uploaded (a marked-up copy that highlights changes made to the original version, and an unmarked version without tracked changes).

Editorial and formatting comments

1. Please amend your current ethics statement to address the following concerns:

a) Did participants provide their written or verbal informed consent to participate in this study?

b) If consent was verbal, please explain i) why written consent was not obtained, ii) how you documented participant consent, and iii) whether the ethics committees/IRB approved this consent procedure.

RESPONSE: The database is comprised of electronic health records from 286 primary healthcare centres. According to the European and Spanish legislation, it is not necessary to ask for informed consent from the participants to use their data for research after anonymization. Thus, the participants did not provide written or verbal informed consent to participate in this study. The study protocol was approved by the Clinical Research Ethics Committee of the IDIAPJGol (P17/212).

We have changed the “Ethics approval and consent to participate” section (page 8-9, lines, 199-211 in the clean copy). The revised statement is:

“This study follows all national and international regulations: Declaration of Helsinki and Principles of Good Research Practice.

In accordance with European and Spanish legislation on confidentiality and data protection ([EU] 2016/679), the data contained in SIDIAP are always pseudonymised. Thus, it is not necessary to ask for informed consent from the participants and so was waived by the Clinical Ethics Committee at IDIAPJGol.

For the link with the CMBD database, SIDIAP uses a third party to ensure confidentiality. The study protocol was approved by the Clinical Research Ethics Committee of IDIAPJGol (P17/212) on November 29, 2017. Anonymity and confidentiality of data and medical records were guaranteed at all times in accordance with the Organic Law 15/1999 on the Protection of Personal Data (http://www.boe.es/boe/dias/1999/12/14/pdfs/A43088-43099.pdf)”.

2. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability

RESPONSE: We do not have a license to publish the databases by the regulation of the Catalan Health Institute. We have changed the “Data Availability Statement” section (pages 21, lines 438-450 in the clean copy). The revised statement is:

‘In accordance with current European and national law, the data used in this study is only available for the researchers participating in this project. The data and variables of this study are obtained from the electronic registries of medical records, which are components of the Information System for Research in Primary Care (SIDIAP) (www.sidiap.org). SIDIAP database and the corresponding research projects were developed thanks to an agreement with the Catalan Health Institute (the owner of the data). Thus, we are not allowed to distribute or make publicly available the data to other parties. However, researchers from public institutions can request data from the SIDIAP and other sources (e.g., Cancer Registries) if they comply with certain requirements. Further information is available online (https://www.sidiap.org/index.php/menu-solicitudes-en/application-proccedure) or by contacting the SIDIAP Team (sidiap@idiapjgol.org)’

3. Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

RESPONSE: Please see response to comment 2

4. Please include your full ethics statement in the ‘Methods’ section of your manuscript file. In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well.

RESPONSE: We have included the new ethics statement in the ‘Methods’ section (page 8-9, lines, 199-211 in the clean copy).

5. Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

RESPONSE: In the new ‘Data Availability Statement’ we explain in detail the ethical or legal restrictions that apply to publicly sharing our data.

Reviewer #1:

1. This article reported results MS is statistically associated with a higher risk of developing at 11 cancer types after the evaluation of 13. Moreover, they demonstrated in this manuscript that the risk of most cancers increased with the number of MS components present in an individual. Hence, the authors finally suggest that prevention strategies targeting individual components of MS could reduce the risk of several cancer types. Although the manuscript is potentially interesting, it is mostly descriptive and most of the conclusions have been published previously (see comments below).

RESPONSE: Thank you for the positive comments. We believe our manuscript provides novel contributions to the scientific community and we show this in our specific responses below.

2. Thus, there are major aspects that could be revised to improve the results:

- The authors should take care in the use of “recent” when they are referring to other published works since I cannot consider a recent work that has been published more than 5 years ago. An example is given, “MS was initially considered a risk factor just for cardiovascular disease. However, recent studies associate MS with a higher risk of liver, colorectal, and bladder cancer in men; and endometrial, pancreatic, colorectal, ovarian, and postmenopausal breast cancer in women”. However, the original manuscript was published in 2012(doi: 10.2337/dc12-0336).

RESPONSE: We agree with the reviewer. We have modified the sentence to exclude the word “recent” (page 3, line 69 and page 3 line73 in the clean copy).

3. The authors did not give too many reasons for the selection of these 13 cancer types. The authors only said, “However, no evidence has been yet provided for the impact of MS components in other less common cancers”. Conversely, for example, lung cancer has related to some metabolic factors of metabolic syndrome. Please, I am really encouraged to provide more reasons for the selection of these cancer types.

RESPONSE: We agree with the reviewer and have modified the paragraph accordingly. The new paragraph is: “We decided to include the most frequent cancer types (ICD-10 codes) in Spain as outcomes. Even though there is evidence of the association between MS and some of these cancer types such as colorectal (C18+20), prostate (C61), liver (C22), bladder (C67), endometrium (C54), pancreas (C25) and breast (C50). Prior studies have not investigated the MS-cancer association for several cancer types using a systematic analysis approach like lung cancer (C34) and kidney cancer (C64) but were included due to their high prevalence in the general population. In addition, we included some less frequently occurring cancer types such as thyroid (C73), Hodgkin lymphoma (C81), non- Hodgkin lymphoma (C82-85) and leukemia (C91-95), for which the current literature is limited. An association between MS and more cancer types than currently recognized in the literature is possible given that the components of MS can trigger biological (hormonal, inflammation, and oxidative stress ) processes involved in tumor development” (page 5, lines 109-121 in the clean copy).

4. The author must justify the fact of comparing every case with four controls since a major number of control cases could imply a major statistical power being really important for the final results and conclusions of this study.

RESPONSE: In case-control studies, the inclusion of more than four or five controls per case provides very little gain in statistical power (Henness S et al.). Thus, we decided to include only four controls per case given that the dataset is already very large and requires a lot of computing power to run the analyses. We included this reference to justify the number of controls. In the ‘Control definition’ section, we add the phrase ‘No more controls were obtained as it has been previously shown that little statistical power is gained by further increasing this ratio (ref)’ (page 6, lines 132-134 in the clean copy)

Reference included: Hennessy S, Bilker WB, Berlin JA et al. Factors influencing the optimal control-to-case ratio in matched case-control studies. Am J Epidemiol. 1999 15;149(2):195-7.

5. In general, the manuscript needs to get a significant improvement in the discussion section. For instance, the sentence is too descriptive: “In gynecological cancers, the menopausal status was a determinant factor, especially in breast cancer. In agreement with previous studies, we observed that MS increased breast cancer risk in postmenopausal women and decreased it in premenopausal women”.

RESPONSE: This is an epidemiological observational study and we tried to be cautious in making any conclusions that could refer to causality. One of our objectives was to provide evidence on the association between MS and cancer, but we need to be careful when making interpretations of such associations. However, considering the reviewer's comment, we tried to include further details in this specific sentence. Specifically, we added information in the paragraph: “In the case of gynaecological cancers, menopausal status was a determinant factor, especially in breast cancer. In agreement with previous studies, we observed that MS increased breast cancer risk in postmenopausal women and decreased it in premenopausal women. Previous investigations proposed that each component of the metabolic syndrome is connected with systemic alterations. Concerning breast cancer, it has been proposed that components of MS, especially obesity, play different roles in cancer risk according to menopausal status and estrogen receptor status. Obesity is associated with decreased risk of estrogen receptor–positive breast cancer in premenopausal women, but it is closely related with increased risk of estrogen receptor–positive breast cancer in postmenopausal women (ref)”. (page 18, lines 353-360 in the clean copy)

References included: Esposito K, Chiodini P, Colao A, et al: Metabolic syndrome and risk of cancer: a systematic review and meta-analysis. Diabetes Care 2012, 35: 2402-2411. 2012;35:2402-2411.

Esposito K, Chiodini P, Capuano A, et al. :Metabolic syndrome and postmenopausal breast cancer: systematic review and meta-analysis. Menopause. 2013 Dec;20(12):1301-9.

Hwang KT, Han KD, Oh S, et al. :Influence of Metabolic Syndrome on Risk of Breast Cancer: A Study Analyzing Nationwide Data from Korean National Health Insurance Service. Cancer Epidemiol Biomarkers Prev 2020, 29: 2038-2047.

We have also included the following paragraph in the discussion section: “Mechanisms that link metabolic syndrome and cancer risk are not fully understood. Metabolic syndrome may be a surrogate marker for other cancer risk factors, such as decreased physical activity, consumption of high–calorie dense foods, high dietary fat intake, low fiber intake, and oxidative stress (ref).” (page 18, lines 367-370 in the clean copy).

Reference included: Esposito K, Chiodini P, Colao A, et al: Metabolic syndrome and risk of cancer: a systematic review and meta-analysis. Diabetes Care 2012, 35: 2402-2411. 2012;35:2402-2411.

6. Concerning the important limitation of not following up with the patients, the authors should clearly explain this limitation and how it could affect the results of the paper.

RESPONSE: There is no follow-up for patients after cancer onset (or index date for controls) in this study. However, data has been treated longitudinally using electronic health records data from healthcare use, which allowed us to determine the timing of exposure in relation to the outcome date. The only limitation in that regard is that we were not able to estimate absolute risks, but only relative risks. We have added a sentence in the paper explaining this limitation “This is a case-control study, when estimating the association between MS and cancer; however, we were not able to estimate cumulative incidences or other types of absolute risks, which would have been useful to put the relative increase in absolute terms”. (page 20, lines 412-415 in the clean copy)

7. Furthermore, the authors should provide information about when the MS component was identified such as previous to tumor diagnose (1 year or 2 years), during tumor treatment…

RESPONSE: Exposure to MS before the event must be at least 2 years prior to cancer diagnosis to avoid reverse causality as it is written in methodological section. We added a phrase in the last paragraph of the “MS definition” section: “Both cases and controls had to be exposed either to MS or to 1 or 2 MS components for at least 2 years before the index date (cancer diagnosis or control identification) to avoid reverse causality” (page 7, line 157 in the clean copy). We did not have information about previous treatment for other health conditions. This is a limitation of our study that we have mentioned in the limitations section. In the paragraph explaining the lack of data, we have added “Also, the lack of data on other possible confounders influencing the relationship between MS and cancer, such as physical activity and parity, or information related to previous treatment for other health conditions might have biased the results” (page 20, line 410 in the clean copy).

Minor comments:

8. A sentence in the abstract is not very clear. “Adjusted conditional logistic regression models were used to estimate OR and 95% CI for the association between individual components of MS and cancer, and MS and cancer.” It seems to be confusing.

RESPONSE: In agreement with the reviewer, we replaced this sentence by the following one: “Adjusted conditional logistic regression models were used to evaluate the association between MS and cancer risk, comparing the effect of global MS versus having one or two individual components of MS”. (Page 2, lines 39-42 in the clean copy)

9. The use of “approximately” when you are talking about the patients available in the study is not accurate for scientific work.

RESPONSE: We agree with the reviewer and have deleted “approximately” accordingly (page 4, line 91 in the clean copy)

10. The authors described in methodology “All individuals ≥ 40-99 years of age with information in the SIDIAP database between 01/01/2006 and 31/12/2017 were included. Final participants included patients with any of the 13 types of incident cancers of interest, together with their paired controls.” Please, the authors should provide the number of patients after the application of each inclusion criteria in the methodology section.

RESPONSE: We agree with the reviewer and have replaced this paragraph with the following: “All individuals ≥ 40 years of age with information in the SIDIAP database between 01/01/2006 and 31/12/2017 were suitable to be included. Patients were excluded from participation when they presented with secondary cancers and metastases.

A total of 190,505 individuals with incident cancer were initially included. Of these, we later excluded 334 men with breast cancer, 6,826 cases because they were diagnosed with more than one cancer on the same day, and 61 because they were >99 years of age on the index date. Finally, a total of 183,284 cases and 733,136 paired controls, four controls for each case, were included (Fig 1).” (pages 4-5, lines 98-105 in the clean copy)

This information was explained at the beginning of the results section, so we have removed it from the results section. We have also removed the word "remaining" from first sentence in the result section so that the sentence is consistent. (page 9, line 214 in the clean copy)

11. Typing errors like “MS component..” should be corrected in the new manuscript.

RESPONSE: The extra period has been removed.

Reviewer #2:

1. In this study, Diana Puente et al studied the potential association between Metabolic syndrome (and number of components met) and the risk of 13 types of cancer. The authors included 183,248 patients from the Information System for Research in Primary Care. The data derived from this study showed that Metabolic Syndrome is associated to 11 cancer types (i.e., endometrial, liver, kidney, pancreas, thyroid, leukaemia, bladder, colorectal, non-Hodgkin lymphoma, lung and post-menopausal breast). These results are interesting for the field and shed light on the relation between Metabolic Syndrome and cancer risk. Therefore, the Reviewer consider that this article is well written, adds to the field valuable information and is suitable to be published in PLOS One.

RESPONSE: We thank the reviewer for the positive comments.

2. However, the following minor comments should be addressed:

The manuscript is well-written. However, some typos can be found in the text and should be corrected:

o Remove intro in line 130.

o Remove intro in line 131.

o Two period symbols in line 133

RESPONSE: We thank the Reviewer for spotting these typos, we have corrected these specific typos and reviewed the manuscript to make sure that there were no other similar mistakes

3. In the introduction there is no information about the inconclusive relation between prostate cancer and metabolic syndrome. Authors should include some information about that in the introduction.

RESPONSE: We have included the following sentence in the introduction section with its respective reference: “The results from studies on prostate cancer and MS are inconclusive, while some of them show an increase in risk(Gomez-Gomez E et al.), others show a reduction(Blanc-Lapierre A et al). (page 3, lines 71-73 in the clean copy)

References included: Gomez-Gomez E, Carrasco-Valiente J, Campos-Hernandez JP et al. Clinical association of metabolic syndrome, C-reactive protein and testosterone levels with clinically significant prostate cancer. J Cell Mol Med 2019, 23: 934-942.

Blanc-Lapierre A, Spence A, Karakiewicz PI et al.: Metabolic syndrome and prostate cancer risk in a population-based case-control study in Montreal, Canada BMC Public Health 2015, 15: 913

4. In addition to the presence of Metabolic Syndrome, the authors also analysed the impact of the number of criteria met on cancer risk. However, no information was shared with regards to the potential association of specific MS criteria and cancer risk. As an example, Gomez-Gomez et al showed that high-blood pressure was associated with clinically significant prostate cancer in their cohort of patients (1). Authors should analyse this or add some information about this in the limitations paragraph.

RESPONSE: This is an interesting issue, and we agree with the reviewer it is important to explore the potential effect of specific components. However, exploring the association of specific MS components and cancer risk was not the main objective of the article (and we did not include it as an objective in the pre-published protocol (Puente D et al.). Additionally, we are providing a comprehensive study looking at the association between single MS components and MS as whole in relation to the risk of numerous types of cancer. The effect of specific components into the risk is likely to vary by cancer type. In order to keep the analyses reasonable for this study, we would prefer to not include this information in the current manuscript. We added some information about this in the limitation paragraph “In addition, it is necessary to explore the potential association of specific MS criteria and risk of specific cancer types in future studies.” (page 20, lines 411-412 in the clean copy).

Published protocol:Puente D, Lopez-Jimenez T, Cos-Claramunt X et al.: Metabolic syndrome and risk of cancer: a study protocol of case-control study using data from the Information System for the Development of Research in Primary Care (SIDIAP) in Catalonia. BMJ Open 2019, 9: e025365

5. The authors performed two sensitivity analyses in which they altered the main definition of Metabolyc Sindrome. The data obtained when using WC instead of BMI are especially interested as WC is a better criterion to ‘measure’ obesity. Although the authors appreciated that the sample is smaller when classifying the patients using WC due to the high number of missing values, this information (number of patients/missing values) is not showed in Supplemental Table 3. Therefore, authors should include this information in the final version of the manuscript.

RESPONSE: We have now included this information at the end of the table “There were 157,872 (86.1%) missing values in the case group and 636,984 (86.9%) missing values in the control group” (page 22, lines 472-743 in the clean copy).

6. The fact that patients with 1 or 2 MS components presented a higher risk of pre-menopausal breast cancer and prostate cancer than participants with MS has not been discussed in the manuscript. Could the authors explain this rare phenomenon? The authors should address this question in the discussion section of the final manuscript.

RESPONSE: Unfortunately, with the data available we could not explore biological mechanisms that could be behind this association. Nevertheless, as indicated in the literature, we found that a study by Gomez-Gomez et al., showed that each of the individual criterion of MS, circulating testosterone levels and inflammatory status may have on the risk and aggressiveness of prostate cancer. Regarding breast cancer, it may be that the presence of a single component could be a risk determinant. Although in our study we cannot determine which component is involved, a study by Xiang Y et al, reported that the hypertriglyceridemic-waist phenotype could be regarded as a strong predictor of breast cancer (ref) and we have included the sentence:” Concerning breast cancer, it has been proposed that components of MS, especially obesity, play different roles in cancer risk according to menopausal status and estrogen receptor status (ref).” (page 18, lines 355-357 in the clean copy) (see response 5 to Reviewer #1)

However, the differences that we find between the presence of 1 or 2 factor and global MS are few and not all of them are statistically significant.

References included: Gomez-Gomez E, Carrasco-Valiente J, Campos-Hernandez JP, et al. Clinical association of metabolic syndrome, C-reactive protein and testosterone levels with clinically significant prostate cancer. J Cell Mol Med 2019, 23: 934-942.

Xiang Y, Zhou W, Duan X, et al. Metabolic Syndrome, and Particularly the Hypertriglyceridemic-Waist Phenotype, Increases Breast Cancer Risk, and Adiponectin Is a Potential Mechanism: A Case-Control Study in Chinese Women. Front Endocrinol (Lausanne) 2019, 10: 905.

Submitted filename: Response to reviewers PLOS ONE.docx

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15 Feb 2022

Association between Metabolic Syndrome and 13 types of Cancer in Catalonia: a matched case-control study

PONE-D-21-30858R1

Dear Dr. Puente,

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Raul M. Luque, PhD

Academic Editor

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Reviewer #1: All comments have been addressed

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Reviewer #2: Yes

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18 Feb 2022

PONE-D-21-30858R1

Association between Metabolic Syndrome and 13 types of Cancer in Catalonia: a matched case-control study

Dear Dr. Puente:

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

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.

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

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on behalf of

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Academic Editor

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