In this longitudinal study of 8593 Iranian adults with a median of 14 years follow-up, the prevalence and clustering of four major MRFs were identified. Moreover, sociodemographic determinants related to cluster membership were identified. We also investigated how different clusters of MRFs were associated with increases or decreases in cancer development. In both genders, seven distinct clusters of four MRFs were identified by SOM. These clusters differed substantially from each other in terms of total number of risk factors, the associations between identified clusters with four sociodemographic factors (age, educational level, physical activity level, and smoking status), and incidence of composite of cancer types. Among men, cluster 3 including those with 100% HSBP had significantly greater risk of incident cancer compared with cluster 5 as the reference group. In females, cluster 6 including individuals with 100% HFPG had significantly higher risk of cancer than cluster 2 as the reference group.
The present study found that the presence of four MRFs, individually or in combination, is highly prevalent in Iranian adults, as we have previously shown [7]. About 88 and 91% of men and women, respectively, had at least one MRF, and 32% of men and 40% of women were found to engage in three or four MRFs.
A large number of studies have previously examined the association between MRFs and cancer incidence. However, they have focused on only one MRF [24, 25] or pre-defined constellation of factors such as metabolic syndrome [8]. In contrast, our study extracted different patterns of MRFs and their effect on cancer risk.
Clustering patterns in men
Among male participants, a relatively healthy subgroup (cluster 7) with the lowest number of MRFs was identified in which 41.6% of subjects had only one MRF. In particular, we identified two unhealthy subgroups (clusters 1 and 4), of whom 100% had at least two MRFs. Association analysis showed that each 1-year increment in age was associated with about 5% increase in chance of being in clusters 1 to 4, with high number of MRFs. It is assumed that aging is the result of the accumulation of multiple forms of damage and pathology in different tissues [26].
Surprisingly, we found that smoking decreased the chance of being in unhealthy clusters (clusters 1 to 6) compared to healthy cluster (cluster 7). In fact, our results suggest that smoking decreases the aggregation of MRFs. Our results confirm the findings of previous studies that suggest that smoking has a protective effect against some MRFs [27]. The inverse association between smoking and clustering of MRFs might be attributable to diminished appetite, rise in metabolic rate, and as a result, lower measures of abdominal obesity and blood pressure among smokers [27].
Recent studies showed a convincing association between metabolic syndrome, as aggregation of three or more metabolic disorders, and certain types of cancer, including prostate [28] and breast [29]. Interestingly, we did not find a clear relationship between the number of MRFs and cancer risk in men. For example, we found the highest incidence rate of cancer in cluster 3, although they had fewer MRFs than clusters 1 and 4. Also, individuals in cluster 7 had the lowest number of MRFs; however, they had a higher incidence rate of cancer compared with cluster 5 (1.7 vs. 0.6). Multivariate Cox regression analyses revealed that cluster 3 (with the highest incidence rate) had more than 3.5-fold the adjusted cancer risk of cluster 5 (with the lowest incidence rate). In complementary analysis (Supplementary Table 1), individuals in cluster 5 had 72% lower risk of cancer after adjustment for confounders compared with cluster 3. Several interesting findings emerge from the patterns identified among men. Firstly, comparison of patterns in clusters 3, 5, 6, and 7 suggests that combined effects of HBMI and HTC (cluster 5) has a more protective effect against cancer risk than individual effects of these two risk factors (clusters 6 and 7). Many studies have investigated the individual effects of BMI and TC on the risk of incident cancer, but research findings have been inconsistent. In a large pooled cohort of Australian adults, BMI was associated with the development of overall, colorectal, and obesity-related cancers in men [30]. Also, several studies have documented a positive association between BMI and risk of prostate cancer [31]. A Chinese cohort study, conducted on 133273 subjects, found a significant association between BMI and risk for cancer incidence. Among men, underweight (BMI < 18.5 kg/m2) increased the risk of gastric and liver cancer, and obesity (BMI ≥ 28.0 kg/m2) increased the risk of colon cancer. However, overweight (BMI 24–28 kg/m2) showed a protective role in lung and bladder cancer incidence in males [32]. Several biological mechanisms have been suggested for the association between HBMI and risk of various cancers. They include obesity-related hormones, growth factors, modulation of energy balance and calorie restriction, multiple signaling pathways, and inflammatory processes affecting cancer cell promotion and progression [33].
In recent years, HTC has been linked to the development of several different cancers although the results are inconsistent. A number of studies have reported a positive association between TC and cancers [24, 34]. However, others found lower overall or site-specific cancer incidence in people with high TC levels [25, 35]. A Korean cohort study showed that a high TC level (≥ 240 mg/dL) was negatively associated with risk of liver, stomach cancer in both men and women, and lung cancer in men [25]. It has been suggested that the observed inverse associations between TC levels and cancer risk is an effect of preclinical cancer or disease due to an increased uptake of cholesterol by tumor cells rather than reflecting a true causal relationship on cholesterol levels [36]. In the present study, we found that co-occurrence of HBMI and HTC put men to a lower risk of cancer compared with the occurrence of the individual factors alone.
Another interesting finding emerges from the comparison of four clusters 1, 2, 3, and 4 with three other clusters (5, 6, and 7); the highest incidence of cancer was observed in clusters at which all or most of the subjects had HSBP (clusters 1 to 4). Also the identified patterns in clusters 3 and 4 suggest that HTC may modify the adverse effect of HSBP, because the risks of cancer were not significantly different between clusters 3 and 4, despite the 100% prevalence of HSBP and HTC in cluster 4. In some studies, arterial hypertension was associated with a higher risk of colorectal [37], prostate [8] cancer, and malignant melanoma [38]. Also, the arterial hypertension was found to be closely linked with renal cell cancer development [39]. There are many uncertainties regarding a possible relation between hypertension and cancer, mainly concerning cancer site specificity, sex, age, and duration of the disease, and also complex interactions with other factors, such as smoking, BMI, diabetes, alcohol, and diet [40]. According to our analysis, the combination of HSBP and HBMI could be conceptualized as a very high risk pattern for overall cancer incidence among Iranian men. From a public health perspective, these results are important due to the high prevalence of hypertension and obesity among Iranian population [7, 41].
Clustering patterns in women
Among females, cluster 1 was found to be relatively healthier than the others, with the lowest mean number of MRFs (0.8). About 43% of women in this cluster had no MRFs. Furthermore, we found two clusters with multiple MRFs (clusters 5 and 7), of whom 100% had at least two MRFs. Association analysis showed a positive relation between aging and clustering of MRFs, similar association was found in men. In addition, moderate education decreased the chance of being in unhealthy clusters (clusters 5, 6, and 7) compared with high education. This finding may be attributable to sedentary lifestyle among highly educated women. One study reported that those with high education had lower total physical activity than those with moderate education [42]. Interestingly, we found that smoking decreased the chance of being in cluster 5, in which all individuals had HBMI, HSBP, and HTC. This suggests the protective effect of smoking on some MRFs [27], as we discussed in the previous section. However, passive smoking increased the chance of being in clusters 3 and 6 with a relatively large number of MRFs. A recent meta-analysis reported a positive association between passive smoking and some cardiometabolic risk factors such as BMI, FPG, and LDL-C which vary with age [43].
In women, the highest incidence rate of cancer was observed in cluster 6, although the number of MRFs was relatively smaller than clusters 5 and 7. Thus, unlike some previous studies [29], our finding did not show a clear relationship between the number of MRFs and cancer risk in women. The results of multivariate Cox regression showed that cluster 6 (with the highest incidence rate) had about 3.6-fold increased risk for cancer compared with cluster 2 (with the lowest incidence rate). Furthermore, cluster 1 had about 2.2-fold increased risk compared with cluster 2 (marginally significant). In complementary analysis (Supplementary Table 1), we found that all clusters, except cluster 1, had significantly lower adjusted risk of cancer compared with cluster 6. Some important conclusions emerge from these findings; firstly, healthy overweight or obese women (cluster 2) showed the lowest overall cancer risk.
Evidence has suggested that BMI is an important predictor of cancer risk [44]: a population-based cohort study of 5.24 million UK adults showed associations between increased BMI and certain types of cancer [45]. In a meta-analysis of 221 datasets, positive associations were reported between HBMI and cancers of the esophagus, thyroid, colon, kidneys, endometrium, and gallbladder; in contrast, increased BMI was negatively associated with lung cancer [46].
The relation between BMI and cancer are complex and are not yet fully understood. For example, some studies have shown that increased BMI is associated with an increased risk of breast cancer in women after menopause [47]; however, a meta-analysis showed that BMI had no significant effect on the incidence of breast cancer during the premenopausal period [48]. In our study, the lowest incidence of cancer in cluster 2 may be due to the age, as this cluster was the youngest group (mean age of 38 years) among 7 clusters.
Very few studies have examined the effects of various combinations of BMI and other MRFs on cancer risk. Our study showed that healthy overweight/obese women (cluster 2) had the lowest risk for incidence of cancer but the risk significantly increased only when HFPG is added to HBMI (clusters 6 and 1). All individuals (100%) and 7.7% of individuals in cluster 6 and cluster 1, respectively, had HFPG; in contrast, nobody had HFPG in cluster 2, reinforcing the positive association between HFPG and cancer risk.
While many observational studies suggest that people with pre-diabetes and diabetes are at a significantly higher risk of some types of cancer [49], but the links between them are incompletely understood. A prospective cohort study of 1,298,385 Koreans (468,615 women) aged 30 to 95 years reported significant positive associations between fasting serum glucose and cancers of the liver and cervix in women [50]. One cohort study in Scotland showed significantly increased risks for pancreatic, liver, and colon cancer in all population, while no significant association was found between diabetes and overall cancer [51]. In conclusion, pre-diabetes/diabetes and cancer have a complex relationship that requires more clinical attention and better-designed studies.
Interestingly, all individuals in cluster 7 had also HFPG; however, no statistically significant differences in risk of cancer were observed between cluster 7 and cluster 2. Unlike cluster 2, all individuals in cluster 7 had HTC which suggests the protective role of HTC against cancer risk, as we have discussed in the previous section.
In sum, we observed the sex-specific risk patterns of overall cancers. It was found that combination of HBMI and HFPG put women at the highest risk for overall cancer. A recent meta-analysis [52] found that diabetic women had a 27% higher risk of cancer compared with non-diabetic women, while men with diabetes had a 19% higher risk of cancer compared with men without diabetes. Also, women with diabetes had a higher risk for most cancers than men with diabetes. The possible mechanisms include as follows: (1) women often spend longer duration than men in the pre-diabetic stage, (2) diabetic women have poor glycemic control compared with men with diabetes, (3) women are often undertreated or not getting the same level of treatment as men. Therefore, women with diabetes may be at greater risk of developing cancer than men due to carcinogenic effects of hyperglycemia and DNA damage [53]. Interestingly, we observed that the presence of two MRFs including HSBP and HBMI significantly increased the risk of total cancer in men. Observational studies have shown inconsistent results for the association between blood pressure and cancer risk. A large study using data from 7 cohort studies investigated the association among 577,799 adults with a mean age of 44 years. The results showed a small increased overall cancer risk only in men [54]. However, a recent meta-analysis showed the positive associations between hypertension and several cancers in both genders such as kidney, colorectal, and breast cancer [55]. Although the association of each isolated metabolic factors with cancer incidence has been investigated in a large number of studies, to our knowledge, no study has been conducted to evaluate the relationship between clusters of MRFs and total cancer in adult population. Therefore, the relation between this risk pattern (HSBP and HBMI) and cancer is poorly understood and needs further carefully designed studies taking into account different combinations of MRFs.
Several limitations should be noted. First, MRFs were measured only once at cohort entry, so we were unable to assess changes over time. Second, due to the small number of cancer cases, we were unable to stratify results by cancer site. Third, the Iranian background of study participants may limit the generalizability of our findings to more diverse ethnic groups of population. Strengths of our study include its long duration of follow-up and population-based sample. Also, a comprehensive physical exam and questionnaire were completed at cohort entry, and complete and reliable outcome data obtained through the outcome committee team. This is the first study that identified multiple clusters of MRFs using SOM in a well-characterized cohort of Iranian population.