Transitions in metabolic syndrome and metabolic obesity status over time and risk of urologic cancer: A Prospective Cohort Study

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Transitions in metabolic syndrome and metabolic obesity status over time and risk of urologic cancer: A Prospective Cohort Study

https://doi.org/10.21203/rs.3.rs-2186469/v1

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The association between metabolic syndrome (MetS) and metabolic obesity (MO) status at baseline, their dynamic changes and the risk of developing urologic cancer (UC) have not been reported.This paper studied 97,897 observation subjects who were free of cancers at baseline (2006−2007). Transitions in MetS and MO status from 2006–2007 to 2008–2009 were considered. The hazard ratios (HRs) and 95% conﬁdence intervals (CIs) for UC were assessed by multifactorial Cox proportional risk regression models. During a median follow-up of 14.02 years, 554 cases of UC were diagnosed. MetS and metabolically unhealthy obesity (MUO) increased the risk of developing UC at baseline. Transitions of MetS to MetS, MUO to MUO and even MUO to metabolically healthy obesity (MHO) over time significantly increased the risk of UC development. Thus, our study provides useful insights into preventing UC.

Health sciences/Oncology/Cancer/Cancer prevention

Biological sciences/Cancer/Cancer epidemiology

Worldwide, urologic malignancies pose a serious threat to human health. Based on GLOBOCAN data, in 2020 alone, the number of new cases of prostate, bladder and kidney cancers reached 1,414,259, 573,278 and 431,288, respectively. In the same year, 375,304, 212,536 and 179,368 deaths resulted from prostate, bladder and kidney cancers, respectively [1]. Although urologic cancer (UC) is highly dangerous, its etiology remains unclear, and known risk factors include age, race, family history of malignancy and smoking [2–4].

Metabolic syndrome (MetS) is a comprehensive syndrome involving blood pressure, glucose and lipid abnormalities, and central obesity. A meta-analysis showed MetS is associated with an increased risk of colorectal, endometrial and postmenopausal breast cancer as well as overall cancers in humans [5] but not with gastric [6] and lung [7] cancer. Mili et al. considered that the association between MetS and cancer may vary depending on the site of cancer [8]. What is the effect of metabolic health status on urinary system cancer? Some studies have found that MetS is associated with an increased risk of bladder, prostate and kidney cancers [9–11]. However, some reports are conflicting [12–13].

Obesity can result from the adverse effects of metabolic abnormalities, such as hyperglycaemia, hypertension and hypercholesterolaemia [14]. Obesity is a well-established risk factor for kidney [15–17], bladder [18, 19] and prostate [20] cancers. Body mass index (BMI) is a common criterion for identifying obesity. These conditions of individuals with or without MetS and obesity are classified into several metabolic obesity (MO) phenotypes. Obesity and MetS can be modified with intervention over time. To prevent development of the whole UC, investigating the effects of MetS, MO phenotypes, and changes in MetS and MO phenotypes over time on UC is necessary.

Therefore, in the present paper, a cohort study based on the Kailuan Study is used to investigate the association between the transitions in MetS, MO phenotypes over time and the risk of UC to provide a theoretical basis for the identification of risk factors and the scientific prevention and control of UC.

Baseline characteristics

The baseline characteristics of the participants classified by MetS and MO status at baseline (2006–2007) and transition in MetS and MO status (2006–2007 to 2008–2009) are shown in Tables 1 and Tables S1–3. In the present cohort, at the baseline (2006–2007) survey, the mean age of the participants was 51.76 ± 12.56 years, and 78,285 (79.97%) were male. A total of 31,336 (32.01%) and 11,046 (11.28%) subjects were defined as MetS and MUO, respectively. Compared with the non-MetS and MHN, the mean age and the proportions of males, subjects with low education level (illiteracy and primary) and heavy salt intake were higher, whereas the proportions of nonsmokers, nondrinkers and subjects with low sitting time (< 4 h/day) were lower in MetS and MUO. MetS to MetS and MUO to MUO compared with non-MetS to non-MetS and MHN to MHN showed the demographic and socioeconomic characteristics of individuals who participated at baseline (2006–2007), and the 2008–2009 survey was consistent with the above.

Association between MetS, MO status at baseline and risk of UC

In this study, the cumulative follow-up period was 1,292,683.09 person–years, the median follow-up time was 14.02 years and 554 participants developed UC from the baseline. Cox proportional hazards regression models were used to assess the effect of MetS and MO status on the development of UC. Individuals with MetS or MUO had significantly higher risk of UC than non-MetS and MHN, and the hazard ratios (HRs) and 95% confidence intervals (95% CI) were 1.26 (1.06–1.49) and 1.49 (1.17–1.89), respectively (Table 2).

Association between transition in MetS, MO status and risk of UC

During a median follow-up time of 11.96 years, from the 2008–2009 survey to December 31, 2020, 316 UC events occurred. For the effect of transition in MetS on the development of UC, compared with non-MetS to non-MetS, MetS to MetS [HRs (95% CI) = 1.45 (1.11–1.88)] had a higher risk of UC, whereas MetS to non-MetS [HRs (95% CI) = 1.29 (0.92–1.82)] had a no statistically significant difference; for transition in MO status, compared with MHN to MHN, MUO to MUO [HRs (95% CI) = 1.60 (1.06–2.42)] had a significantly higher risk of UC, and similarly, MUO to MHO [HRs (95% CI) = 2.65 (1.43–4.92)] showed a higher risk of UC (Table 3).

Subgroup analyses of MetS, MO status, and transitions in MetS and MO status and the risk of UC

In analyses stratified by age, gender and smoking status, the effect of MetS, MO status at baseline and transition in MetS on the risk of UC by age displayed a significant difference (p for interaction < 0.05). Subgroup analyses indicated low-age (< 55 years old), male subjects, and former and current smokers with MetS and MUO at baseline had an increased risk of UC than non-MetS and MHN. Similarly, for transitions in MetS and MO status, the risk of developing UC of subjects with MetS to MetS and MUO to MUO were significantly higher than non-MetS to non-MetS and MHN to MHN in low-age (< 55 years old), male, and former and current smoking subgroups, whereas in former and current smokers, participants with MetS to non-MetS, the effect still had statistically significant differences (Tables S4–7).

Sensitivity analyses of the relationship between MetS, MO status, and their transitions and the risk of UC

In the sensitivity analyses, the associations of MetS, MO status, and their transitions and the risk of developing UC did not materially change compared with the main models after excluding participants with a history of myocardial infarction and stroke, subjects who developed UC within the first two years of follow up or individuals with less than 1.5 years between baseline (2006–2007) and 2008–2009 surveys (only for the transitions in MetS and MHO status), as shown in Tables S8–9.

Amongst Kailuan Study participants, having MetS and MUO at baseline was associated with a risk for incident UC; the transitions in MetS and MO status over time (2006–2007 to 2008–2009) revealed the risk of UC was strongly higher in subjects with MetS to MetS, MUO to MHO and MUO to MUO. Individuals with MetS to non-MetS had a higher risk, but the difference was not statistically significant.

The association between MetS and MO status and the risk of UC has not been reported in previous studies. In this paper, MetS at baseline increased the risk of developing UC by 26% compared with the non-MetS population. This result was similar to that observed in studies of a certain part of the UC, such as bladder [9], prostate [10] and kidney [11] cancer, although some inconsistent research reports [12, 13] were noted. These differences in findings may be explained by dissimilarities in the observed populations, races, study designs, the length of the follow-up period, and adjustments for confounding factors and definitions of MetS. MetS and obesity frequently coexist, and obesity is a risk factor for UC [15–20]. Amongst MO phenotypes, MUO subjects had a 49% increased risk of UC at baseline than MHN subjects. A Korean study [21] showed that participants with metabolic unhealth obesity had a higher risk of prostate cancer as well. The results of MetS and MUO on promoting the development of UC can be explained as follows: On the one hand, they share many common risk factors, including older age, obesity, lack of exercise, unhealthy diet, disturbance of the biological clock, oxidative stress, air pollution and damage caused by exposure to substances that disrupt the endocrine system [22, 23]; on the other hand, chronic inflammation determined by obesity may concur to the development of tumours, and insulin resistance may directly or indirectly enhance cell proliferation [24].

MetS and MO status can be changed over time and with certain interventions. In a subsequent study, the influence of transitions in MetS and MO status (2006–2007 to 2008–2009) on the risk of developing UC was explored. It showed that compared with non-MetS to non-MetS, the UC risk of MetS to MetS significantly increased by 45%. The risk of developing UC of participants with MetS at baseline (2006–2007) that changed into non-MetS (2008–2009 survey) was higher, but the difference was not statistically significant. These results confirm MetS is a risk factor of UC, and the improvement of MetS can reduce the risk of UC, which provides a scientific basis for the prevention of UC. In the study of MO status transition, compared with MHN to MHN, the UC risk of MUO to MUO was significantly increased [HR (95% CI) = 1.60 (1.06–2.42)]. Moreover, when the status of MUO changed to MHO, the metabolic unhealth status improved, but the UC risk was still significantly higher. Therefore, the reduction of BMI in patients with MUO whilst controlling MetS is of realistic significance to prevent the occurrence of UC.

Subgroup analyses showed that in low-age (< 55 years old), male, and former and current smoking subgroup, baseline MetS and MUO and transitions of MetS to MetS and MUO to MUO significantly increased the risk of UC compared with controls, especially in the smoking subgroup, and even the effect of MetS to non-MetS on the incident of UC was statistically significant. Such results suggest that the prevention and control of MetS and obesity should be strengthened in low-age, male individuals. Moreover, the harm of tobacco should be reduced, and smoking bans are of great significance for the prevention and control of UC.

The strengths of this study are large sample size, prospective cohort design and robust follow-up mechanism. In addition, this is the first study to explore whether MetS, MO status and their transitions influence the incident of the whole of urinary system cancers. Our study also has some limitations. Firstly, the female participants accounted for a lower percentage (20.03%) than the male participants, and the gender imbalance may influence the generalizability of the results. Secondly, the transition intervals of MetS and MO phenotypes were relatively short. The sensitivity analysis excluded participants whose interval between two surveys was less than 1.5 years. Although the results were still stable, follow up needs to continue, more cases must be accumulated and the transition intervals should be lengthened, which are more meaningful to explain the results.

In conclusion, the presence of MetS or MUO at baseline increased the risk of UC, and the participants with transitions of MetS to MetS, MUO to MUO and even MUO to MHO over time had a significantly higher risk of UC. These findings might provide the scientific basis for preventing UC by maintaining metabolic health and controlling obesity.

Study Cohort

The Kailuan Study is an ongoing cohort study based on functional community population that began in 2006. All the participants underwent questionnaires interviews, clinical examinations and laboratory tests every 2 years at 11 Kailuan Group affiliated hospitals by well-trained physicians or nurses using a standardized protocol, and follow-up data were obtained for events including UC, allowing us to investigate the relationship between transitions in MetS and MO phenotypes and UC. More details about the Kailuan Study can be found elsewhere [25, 26]. Participants who met the following criteria were recruited in this study: (1) included in the baseline survey (2006–2007) population of the Kailuan Study, (2) age ≥ 18 years and (3) signed the informed consent form. The exclusion criterion was history of malignancy. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Kailuan General Hospital.

A total of 101,510 participants (81,110 males and 20,400 females; aged 18 − 98 years) met the criteria and were recruited in this study, and 379 participants with a history of cancer at baseline (2006 − 2007), 1,468 participants with missing data on BMI and 1,766 participants with missing data on MetS status were excluded. Finally, 97,897 participants were included in the analysis of the association between MetS and MO status at baseline and the risk of UC. To explore the effects of transitions in MetS and MO status over time (from 2006–2007 to 2008 − 2009) on the risk of UC, 35,178 participants, amongst whom 24,162 did not participate in the 2008 − 2009 survey, 34 with incident UC between the 2006 − 2007 and 2008 − 2009 surveys, 3,186 participants with missing data on BMI and 7,796 participants with missing data on MetS components in the 2008–2009 survey were further excluded, leaving 62,719 participants for assessing the association of transitions in MetS and MO status and risk of UC (Fig. 1).

Collection of Exposure Information

The baseline data included sociodemographic characteristics (age, gender, occupation, education level, economic income and marital status), lifestyle characteristics (smoking status, alcohol consumption, salt intake and sitting time), history of previous diseases, physical examination data [body weight, height, waist circumference (WC) and blood pressure (BP)] and blood indices (fasting glucose and lipids).

Participants wore light clothing, removed their shoes and hats, and had their body weight and height measured. BMI was calculated by weight in kilograms divided by height in square meters. WC was measured at the level of the midpoint between the anterior superior iliac crest and the lower rib cage. Systolic and diastolic blood pressure were measured using a mercury sphygmomanometer with a suitable cuff on the left arm of the subject after 5 min of rest and then again after 5 min, and the average of the two measurements was recorded. Early morning fasting blood samples were collected from the subjects to measure blood glucose and lipids. Fasting blood glucose level was measured using the hexokinase/glucose-6-phosphate dehydrogenase method, and the coefficient of variance of blind quality control samples was < 2.0%. Triglycerides were determined by glycerol phosphate oxidase assay (coefficient of mutual variation < 10%). After the precipitation of apolipoprotein B with dextrose sulphate and magnesium chloride, high-density lipoprotein cholesterol level was measured in the supernatant [26].

Definition of Variables

MetS was defined according to the harmonized International Diabetes Federation criteria [27], as shown in Table 1. Obesity statuses were categorized according to the Working Group on Obesity in China into normal weight (BMI < 28 kg/m²) and obesity (BMI ≥ 28 kg/m²) [28]. MO status was classified into four phenotypes according to whether the subject had MetS or obesity (Table 4).

According to MetS and obesity status at baseline (2006–2007) and 2008–2009 survey, transitions in MetS were divided into four phenotypes: non-MetS to non-MetS, non-MetS to MetS, MetS to non-MetS and MetS to MetS. Transitions in MO status were classified into seven phenotypes: MHN to MHN, MHO to MHO, MHO to MUO, MUN to MUO, MUO to MHO, MUO to MUN and MUO to MUO.

Smoking status was classified as follows: never smoker, former smoker (has quit smoking for more than 12 months) and current smoker (smokes one or more cigarettes per week for not less than 12 consecutive months). Alcohol consumption status was divided into the following categories: never, former (abstained from alcohol for more than six months) and current alcohol consumption (one or more drinks per month for no less than six months in a row).

Collection of Endpoint Event Information

The follow-up period started when the participants were recruited and the baseline survey was performed (2006–2007). The last follow up was completed on December 31, 2020. The follow-up endpoint event was a new UC or death in the observed subject (whichever came first). Firstly, the information of participants’ medical records was obtained from the Tangshan City Health Insurance System. Professionally trained investigators then went to the hospitals to collect information on the subjects’ medical history. Clinicians verified the pathology, imaging (including magnetic resonance imaging, computed tomography and colour Doppler ultrasonography) and blood biochemical examination results to confirm and refine the diagnosis of UC. Tumour cases were encoded according to the International Classification of Diseases-10. UC included prostate cancer, kidney cancer, carcinoma renal pelvis, ureteral cancer, bladder cancer and urethral cancer with codes C61 and C64–C68. Information on death events was obtained from the Kailuan Group Social Insurance System.

Statistical analysis

The baseline characteristics of MetS, MO phenotypes, and transitions in MetS and MO status were determined. Continuous variables were expressed as mean ± standard deviation (SD), whereas categorical variables were expressed as the number of cases (percentage). Multiple imputation methods were used to replace the missing data for covariates.

Multifactorial Cox proportional risk regression models were used to analyse the relationships between MetS (non-MetS group as the reference) and MO phenotypes (MHN group as the reference) at baseline (2006–2007) and the risk of developing UC. The association between the transitions in MetS (compared with non-MetS to non-MetS individuals) and MO status (compared with MHN to MHN individuals) from baseline (2006 − 2007) to 2008 − 2009 survey and the risk of UC incidence during the follow up (after 2008 − 2009) was also assessed in the same way. Model 1 was adjusted for age (at the start of follow up) and gender, whereas Model 2 was further adjusted for smoking status, alcohol consumption, occupation, education level, income, marital status, salt intake and sitting time.

Subgroup analyses were conducted to explore the associations of MetS, MO status and their transitions with the risk of UC according to age, gender and smoking status, Multiplication interactions were performed as well.

Several sensitivity analyses were performed to examine the robustness of the results excluding participants who (1) had a history of myocardial infarction and stroke, (2) developed UC within the first 2 years of follow up and (3) had less than 1.5 years between baseline (2006–2007) and 2008–2009 surveys (only for transitions in MetS and MO status ).

All statistical analyses were performed using SAS 9.4 and were considered statistically significant at P < 0.05 (two sided).

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

The statistical code developed for this study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no competing interests.

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Table 1

Baseline characteristics of participants by MetS status, 2006–2007.
Characteristics	Total cohort (n = 97897)	Non-MetS (n = 66561)	MetS (n = 31336)	p
Age(years,mean ± SD)	51.76 ± 12.56	50.46 ± 12.86	54.51 ± 11.41	< 0.0001
Gender, n(%)
Female	19612(20.03)	13969(20.99)	5643(18.01)	< 0.0001
Male	78285(79.97)	52592(79.01)	25693(81.99)	< 0.0001
Smoking status, n(%)
Never	58327(59.58)	40165(60.34)	18162(57.96)	< 0.0001
Former	5867(5.99)	3463(5.20)	2404(7.67)
Current	33703(34.43)	22933(34.45)	10770(34.37)
Alcohol consumption, n(%)
Never	57442(58.68)	39332(59.09)	18110(57.79)	< 0.0001
Former	4003(4.09)	2362(3.55)	1641(5.24)
Current	36452(37.24)	24867(37.36)	11585(36.97)
Occupation, n(%)
White collar	7884(8.05)	5269(7.92)	2615(8.35)	0.0214
Blue collar	90013(91.95)	61292(92.08)	28721(91.65)	0.0214
Education level, n(%)
Illiteracy and primary	10964(11.20)	6707(10.08)	4357(13.59)	< 0.0001
Middle school	79901(81.62)	54560(81.97)	25341(80.87)
College and above	7032(7.18)	5294(7.95)	1738(5.55)
Income(yuan per psrson per month), n(%)
< 600	27897(28.50)	18575(27.91)	9322(29.75)	< 0.0001
≥ 600-<1000	62410(63.75)	42881(64.42)	19529(62.32)
≥ 1000	7590(7.75)	5105(7.67)	2485(7.93)
Marital status, n(%)
Single	4420(4.51)	3197(4.80)	1223(3.90)	< 0.0001
Married/cohabiting	93477(95.49)	63364(95.20)	30113(96.10)	< 0.0001
Salt intake, n(%)
Light	9201(9.40)	6240(9.37)	2961(9.45)	< 0.0001
General	78122(79.80)	53635(80.58)	24487(78.14)
Heavy	10574(10.80)	6686(10.04)	3888(12.41)
Sitting time(h/day), n(%)
< 4	72714(74.28)	49721(74.70)	22993(73.38)	< 0.0001
≥ 4-<8	22070(22.54)	14746(22.15)	7324(23.37)
≥ 8	3113(3.18)	2094(3.15)	1019(3.25)

MetS, metabolic syndrome.

Table 2

Associations between MetS and MO status at baseline (2006–2007) and risk of UC.
Groups		Total cases	Person years	Incident cases	HR(95%CI)
Groups		Total cases	Person years	Incident cases	Model 1	Model 2
MetS status	Non-MetS	66561	886385.16	332	Ref	Ref
MetS status	MetS	31336	406297.93	222	1.25(1.05–1.48)	1.26(1.06–1.49)
MO status	MHN	59385	790282.21	303	Ref	Ref
	MHO	7176	96102.96	29	0.87(0.60–1.28)	0.89(0.61–1.30)
	MUN	20290	261965.90	136	1.12(0.92–1.37)	1.130(0.92–1.38)
	MUO	11046	144332.02	86	1.49(1.15–1.86)	1.49(1.17–1.89)

MetS, metabolic syndrome; MO, metabolic obesity; UC, urologic cancer; MHN, metabolically healthy normal weight; MHO, metabolically healthy obesity; MUN, metabolically unhealthy normal weight; MUO, metabolically unhealthy obesity; HR, hazard ratio; CI, conﬁdence interval.

Model 1 was adjusted for age and gender; Model 2 was further adjusted for smoking status, alcohol consumption, occupation, education level, income, marital status, salt intake and sitting time.

Table 3

Associations between transitions in MetS and MO status (2006–2007 to 2008–2009) and risk of UC.
Groups		Total cases	Person years	Incident cases	HR(95%CI)
Groups		Total cases	Person years	Incident cases	Model 1	Model 2
Transition in MetS status
MetS status at baseline (2006–2007)	MetS status at follow-up (2008–2009)
Non-MetS	Non-MetS	32826	378952.69	139	Ref	Ref
Non-MetS	MetS	9905	112711.12	41	0.84(0.60–1.20)	0.85(0.60–1.21)
MetS	Non-MetS	7025	78977.25	44	1.28(0.91–1.79)	1.29(0.92–1.82)
MetS	MetS	12963	144280.66	92	1.42(1.09–1.85)	1.45(1.11–1.88)
Transition in MHO status
MHO status at baseline (2006–2007)	MHO status at follow-up (2008–2009)
MHN	MHN	29218	337247.96	121	Ref	Ref
MHO	MHO	1437	16723.51	4	0.80(0.30–2.17)	0.82(0.30–2.22)
MHO	MUO	1423	16445.79	8	1.34(0.65–2.74)	1.34(0.65–2.74)
MUN	MUO	958	10503.11	7	1.40(0.66–3.01)	1.42(0.66–3.05)
MUO	MHO	1005	11443.27	11	2.60(1.40–4.82)	2.65(1.43–4.92)
MUO	MUN	1355	15154.18	10	1.55(0.81–2.95)	1.56(0.82–2.97)
MUO	MUO	3992	44897.53	28	1.57(1.04–2.37)	1.60(1.06–2.42)

MetS, metabolic syndrome; MO, metabolic obesity; UC, urologic cancer; MHN, metabolically healthy normal weight; MHO, metabolically healthy obesity;MUN, metabolically unhealthy normal weight; MUO, metabolically unhealthy obesity; HR, hazard ratio; CI, conﬁdence interval.

Model 1 was adjusted for age and gender; Model 2 was further adjusted for smoking status, alcohol consumption, occupation, education level, income, marital status, salt intake and sitting time.

Table 4

Definitions of MetS and MO phenotypes.
Harmonized International Diabetes Federation criteria for MetS: ≥3 of the following components:
(1) central obesity, defined as WC ≥ 90 cm in men and WC ≥ 80 cm in women
(2) elevated TG, defined as TG ≥ 150 mg/dL (1.7 mmol/L) and/or drug treatment for elevated TG
(3) reduced HDL-C, defined as HDL-C < 40 mg/dL (1.0 mmol/L) in males and HDL-C < 50 mg/dL (1.3 mmol/L) in famales and/or durg treatment for reduced HDL-C
(4) elevated BP, defined as systolic BP ≥ 130 mmHg and/or diastolic BP ≥ 85 mmHg, and/or a history of hypertension treated with antihypertensive drugs
(5) elevated FBG, defined as FBG ≥ 100 mg/dL (5.6 mmol/L) and/or receiving glucose-lowering medication for elevated glucose
Metabolic obesity phenotypes:
MHN: BMI < 28 without MetS
MUN: BMI < 28 with MetS
MHO: BMI ≥ 28 without MetS
MUO: BMI ≥ 28 with MetS

MetS, metabolic syndrome; MO, metabolic obesity; WC, waist circumference; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; BP, blood pressure; FBG, fasting blood glucose; MHN, metabolically healthy normal weight; MUN, metabolically unhealthy normal weight; MHO, metabolically healthy obesity; MUO, metabolically unhealthy obesity

There is NO Competing Interest.

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Transitions in metabolic syndrome and metabolic obesity status over time and risk of urologic cancer: A Prospective Cohort Study

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