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. 2018 Oct 17;119(8):1018–1027. doi: 10.1038/s41416-018-0214-7

Waist circumference and risk of 23 site-specific cancers: a population-based cohort study of Korean adults

Kyu Rae Lee 1, Mi Hae Seo 2, Kyung Do Han 3, Jinhyung Jung 3, In Cheol Hwang 4,✉; on behalf of the Taskforce Team of the Obesity Fact Sheet of the Korean Society for the Study of Obesity
PMCID: PMC6203821  PMID: 30327562

Abstract

Background

Large waist circumference (WC) is a risk factor for several site-specific cancers, but a large-scale systematic investigation across all common cancers adjusted for potential confounders has not been conducted. This study aimed to evaluate the possible links between WC and common cancers.

Methods

We prospectively examined the association between WC and the risk of cancers in a 7-year cohort study of nearly 22.9 million Korean adults. Using the claims database merged with the national health check-up data, we fitted proportional hazard models to investigate associations between WC and 23 of the most common cancers, with adjustment for potential confounders, including body mass index (BMI). We also evaluated the modification of BMI on the relationships between WC and the incidence of cancer.

Results

A total of 769,871 cancer cases were identified. WC was positively associated with 18 of 23 cancers, and the effects varied substantially by site in each sex. The modification of BMI on the WC-cancer association also varied across the cancer site; in most cases it mitigated the association. For cancers of the oral cavity, larynx, oesophagus, lung, and premenopausal breast, the BMI adjustment reversed the association toward being positive (all Ptrend < 0.001).

Conclusions

Central obesity, independent of general obesity, was associated with the risk of several cancers. The heterogeneity in the mediating effects of BMI suggests that different mechanisms are associated with different cancer sites. Based upon these findings, active strategies to monitor and prevent central obesity should be implemented.

Subject terms: Cancer epidemiology, Risk factors

Introduction

Obesity is an established risk factor for mortality and various chronic diseases. A pooled analysis of 57 prospective studies has documented that each 5 kg/m2 higher body mass index (BMI) was associated with a 29% increase in mortality1. Furthermore, results from the EPIC (European Prospective Investigation into Cancer and Nutrition) emphasised the additive use of the central obesity index on predicting the risk of death2. Given the inexorable rise in obesity worldwide in recent years3, understanding the effects of obesity on major health outcomes is urgent and has not been sufficiently emphasised.

There is increasing evidence that obesity increases the risk of certain types of cancers4,5. However, most researchers have emphasised the role of overall obesity (generally assessed as BMI), rather than central obesity, in the etiologies of these malignancies6. Because metabolic derangement supported by insulin and insulin-like growth factor (IGF) is a possible mechanism in carcinogenesis7, central obesity may present a higher threat to the risk of cancer than to general obesity. Moreover, with regard to tumour angiogenesis and cell proliferation, intra-abdominal fat has been hypothesised to be biologically different from fat in other areas8,9.

Comprehensive reviews have recently suggested that central obesity increases the cancer risk in various sites, including the gastrointestinal tract10–12, biliary tract13, lung14, breast15, thyroid16, head and neck17, and genital tract18–20. However, there are important limitations to these studies. Individual studies have often had insufficient power, and potential confounders have been inconsistent across studies. In addition, many studies have used self-reported WC data, which probably underestimated the true WC, and there have been few reports of the effects of BMI adjustment on the associations between WC and cancer risk21,22. Furthermore, cumulative meta-analyses of observational studies have inherent limitations, including publication biases and heterogeneity23,24.

Our aim was therefore to investigate the possible associations between WC and the most common site-specific cancers in a single population using a large-scale nationwide claims database. To better understand the role of abdominal obesity on cancer development, we additionally examined the effects of BMI adjustment on the WC-cancer association.

Methods

Design and participants

We used the Korean nationwide claims database and biennial medical examination data provided by the Korean National Health Insurance Corporation (NHIC). In the Republic of Korea, comprehensive medical care of nearly all Koreans (97%) is covered by single insurance from the NHIC. The information contained the enrollees’ demographics, utilisation of medical facilities, disease code registered by clinicians, and pharmacy dispensing claims. Data from medical examinations included health-related surveys using standardised questionnaires, height, weight, blood pressure, and fasting laboratory findings, such as serum glucose and total cholesterol. The WC measurements were added in 2009. Using the survey dataset, we extracted information about the medical history and health-related habits such as smoking, alcohol consumption, and physical activity. The questionnaires, which were reviewed by a trained staff, were self-reported.

We used the data of a 7-year cohort (2009–2015) provided by the NHIC for research purposes. We identified 23,452,862 adults ≥ 20 years of age who received the national health examination service at least once from 2009 to 2012. Individuals who had been diagnosed with any cancer (n = 448,468, 1.9%) before 31 December 2008 or who had any missing data on baseline characteristics (n = 125,189, 0.5%) were excluded. Finally, 22,879,205 adults were followed-up to the date of any cancer diagnosis, or death or 31 December 2015, whichever was first. For uterine or ovarian cancer, we censored women undergoing hysterectomy or oophorectomy due to benign causes at that time point, respectively. The follow up was a mean of 5.3 ± 1.2 years ( ± standard deviation) after the WC measurements. During the study follow up, 769,871 cancers occurred (385,200 among males; 384,671 among females): 10,981 in oral cavity, 8746 in oesophagus, 132,593 in stomach, 149,397 in colorectum, 61,673 in liver, 20,172 in biliary tract, 45,927 in pancreas, 4926 in larynx, 72,133 in lung, 2963 malignant melanoma, 74,835 in breast, 15,842 in cervix, 9726 in uterus, 13,155 in ovaries, 52,492 in prostate, 1505 in testis, 17,204 in kidney, 20,251 in bladder, 10,617 in brain, 156,315 in thyroid, 15,325 lymphoma, 5216 multiple myeloma, and 8576 leukaemia. Previous studies have already used the NHIC database25,26.

Data collection and processing

The primary outcome was newly developed cancer. The diagnosis of cancer was ascertained by the Serious Disease Registry, a nationwide registry identifying persons who needed greater medical expense benefits because of serious or rare diseases, including malignant neoplasm25,27. Within the program, the NHIC had sent specific diagnostic criteria to physicians for copayment reductions, and health institutions were required to review the physicians’ diagnoses. Therefore, misclassification is negligible, and the cancer diagnosis is considered valid. Cancer cases were classified as follows according to the International Classification of Disease for Oncology-10th edition (ICD-10): oral cavity (C00–14), oesophagus (C15), stomach (C16), colorectum (C18–21), liver (C22), biliary tract (C23–24), pancreas (C25), larynx (C32), lung (C33–34), malignant melanoma (C43), breast (C50), cervix (C53), uterus (C54–55), ovaries (C56), prostate (C61), testis (C62), kidney (C64), bladder (C67), brain (C70-72), thyroid (C73), lymphoma (C82–86), multiple myeloma (C90), and leukaemia (C91–95).

BMI and WC were used as an index of overall and central adiposity, respectively. The data on anthropometry were collected by direct measurements at medical institutions equipped with facilities and staff approved by the regulations defined by the KNHIC28. The categories of WC were based on quintiles in our cohort. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Individuals were categorised into four groups according to BMI following the World Health Organization recommendations for Asians as follows: underweight, <18.5; normal, 18.5–22.9; overweight, 23.0–24.9; and obese, ≥25.029.

Menopausal status was set at the age of 50 years, representing the usual menopausal age of Korean women30. Socioeconomic status, estimated by the average insurance premium per month, was classified into quartiles. Smoking status was categorised into three groups as a non-, former, or current smoker. Alcohol consumption was categorised into three groups as a non-, moderate (<30 g per day), or heavy drinker (≥30 g per day). Regular physical activity was defined as exercise ≥one session per week. A comorbid condition was defined primarily based on the combination of past history and the use of ≥1 drugs for the corresponding disease, which included hypertension (ICD-10 code, I10–13/15), type 2 diabetes (E11–14), and hyperlipidemia (E78). Individuals who had abnormal findings in the health examination were considered as patients with the corresponding parameters: ≥140/90 mmHg of blood pressure, 126 mg/dL of fasting plasma glucose, and 240 mg/dL of total cholesterol.

Statistical analysis

Analyses were performed separately by sex, as appropriate. Cox proportional hazards models, with attained age as the underlying time metric, estimated the hazard ratio (HR) and 99% confidence interval (CI) for the associations of WC with cancer risk, considering potential confounders such as age (continuous), three health-related habits, three co-morbidities, and the BMI (continuous). In the Cox model, smoking status and alcohol use were included as 3 categories, respectively. The follow-up period began on the date of anthropometric assessment. In addition, the WC results were presented with and without adjustment for the BMI. All Cox models were tested for and met the proportional hazards assumption. SAS software, version 9.4 (SAS Institute, Cary, NC, USA) was used for all statistical analyses. A two-sided p-value < 0.05 was considered statistically significant.

Results

Table 1 lists the baseline characteristics of participants across the WC quintiles by sex. Centrally obese individuals were more likely to be elderly and to have a high BMI and increased comorbidites. Current smoking was inversely associated with WC categories, and heavy drinkers were more common in centrally obese males and centrally lean females.

Table 1.

Baseline characteristics by waist circumference quintile (cm) in the Korean National Health Insurance Cohort, 2009–2015

% Male Female
Q1 (≤76) Q2 (77–81) Q3 (82–85) Q4 (86–90) Q5 (≥91) Q1 (≤68) Q2 (69–73) Q3 (74–77) Q4 (78–83) Q5 (≥84)
No. 2,160,039 2,536,624 2,294,267 2,511,520 2,148,838 2,309,344 2,330,322 1,908,544 2,354,680 2,325,027
Follow up, years 5.4 ± 1.2 5.4 ± 1.2 5.4 ± 1.2 5.4 ± 1.2 5.3 ± 1.2 5.2 ± 1.2 5.3 ± 1.2 5.3 ± 1.2 5.3 ± 1.2 5.3 ± 1.3
Age, year
  20–39 49.4 39.0 31.2 27.4 27.7 52.9 29.5 17.7 11.0 8.2
  40–59 35.8 44.9 50.0 50.9 48.1 40.8 57.7 60.4 55.5 45.1
  60–79 13.6 15.4 18.3 20.8 23.2 5.5 11.8 20.5 31.5 43.6
  ≥80 1.2 0.9 0.8 0.9 1.0 0.8 1.0 1.4 2.1 3.1
  Mean ± SD 42.1 ± 14.8 44.8 ± 13.8 47.0 ± 13.4 48.3 ± 13.3 48.8 ± 13.7 38.5 ± 12.7 45.0 ± 13.0 49.6 ± 13.0 53.7 ± 13.0 57.2 ± 13.3
Body mass index, kg/m2
  <18.5 11.4 0.8 0.2 0.1 0.03 23.7 3.3 1.0 0.4 0.1
  18.5–22.9 76.9 54.4 26.6 9.2 1.5 73.2 76.0 51.0 25.7 5.8
  23.0–24.9 10.0 34.2 44.1 32.5 9.5 2.7 17.5 34.4 37.7 17.2
  ≥25.0 1.6 10.6 29.1 58.3 89.0 0.4 3.2 13.7 36.2 76.8
  Mean ± SD 20.8 ± 1.9 22.8 ± 1.8 24.1 ± 1.8 25.4 ± 1.9 27.9 ± 2.6 19.7 ± 1.7 21.6 ± 1.8 23.0 ± 2.0 24.4 ± 2.2 27.2 ± 3.1
 Current smoking 50.9 46.1 43.4 42.1 42.3 4.9 4.2 3.8 3.6 3.9
 Heavy drinking 11.0 13.0 14.0 15.3 17.4 1.4 1.3 1.2 1.1 1.1
 Regular physical activity 17.9 20.2 20.7 20.4 19.2 12.3 15.7 16.9 16.9 15.3
 Hypertension 13.3 20.2 26.9 33.9 45.3 6.2 13.1 21.5 32.1 48.6
 Type 2 diabetes 4.8 7.5 10.2 12.9 17.9 1.5 3.3 5.9 9.8 17.8
 Hyperlipidemia 7.6 13.4 18.2 22.4 27.9 6.7 13.0 19.8 27.1 36.0
 Menopause — — — — — 18.3 34.9 50.1 63.1 73.2
 Socioeconomic status, lowa 20.2 17.4 16.8 16.8 17.7 23.6 24.9 25.2 25.0 25.1
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SD standard deviation.

All Ptrend < 0.001.

aLowest quartile of the insurance premium

Table 2 shows the HRs for developing cancers compared with individuals with the lowest WC (quintile 1), after controlling for confounding factors including the BMI. Figure 1 also depicts the forest plot for each cancer across WC quintiles before and after BMI adjustment. The data confirmed that in the Korean population, central obesity strongly increased the risk of cancer in the stomach, colorectum, hepatobiliary system, kidney, thyroid, brain, and lymphoma in a dose-dependent manner (Ptrend < 0.001 for both sexes). Gender specific differences in significance were notable in some cancers, with more robust associations in males for cancers of the head and neck, oesophagus, pancreas, lung, bladder and skin, and more robust correlations in females for multiple myeloma. There were also significant dose-dependent relationships between WC and cancers of the prostate (Ptrend < 0.001) and breast (premenopausal, Ptrend = 0.007; postmenopausal, inversely Ptrend = 0.035), but associations with other genital tract cancers were not reach the significant. The BMI did not affect or attenuated the WC-cancer association for most cancers. However, for cancers of the oral cavity, larynx, oesophagus, and lung in male (all Ptrend < 0.001) and premenopausal breast (Ptrend = 0.007), the BMI adjustment reversed the association toward being positive.

Table 2.

Multivariate hazard ratio and 99% confidence interval for developing cancers by waist circumference quintile

Cancer site Male Female
Q1 Q2 Q3 Q4 Q5 P trend Q1 Q2 Q3 Q4 Q5 P trend
Overall
  No. cancer 52,220 72,638 76,014 94,007 90,321 49,394 69,656 67,691 93,804 104,126
  HRa (99% CI) 1 1.09 (1.07–1.11) 1.14 (1.12–1.15) 1.19 (1.17–1.20) 1.26 (1.24–1.28) <0.001 1 1.20 (1.18–1.22) 1.27 (1.25–1.29) 1.30 (1.28–1.32) 1.35 (1.33–1.37) <0.001
  HRb (99% CI) 1 1.10 (1.08–1.12) 1.15 (1.14–1.17) 1.21 (1.19–1.23) 1.30 (1.27–1.33) <0.001 1 1.15 (1.13–1.17) 1.19 (1.17–1.21) 1.17 (1.15–1.19) 1.14 (1.12–1.17) <0.001
Oral cavity
  No. cancer 1245 1619 1560 1829 1656 371 533 525 784 859
  HRa (99% CI) 1 1.02 (0.92–1.12) 0.98 (0.89–1.09) 0.98 (0.89–1.08) 0.98 (0.89–1.08) 0.331 1 1.11 (0.93–1.33) 1.12 (0.94–1.34) 1.16 (0.98–1.38) 1.12 (0.94–1.34) 0.129
  HRb (99% CI) 1 1.13 (1.02–1.25) 1.16 (1.04–1.30) 1.23 (1.10–1.39) 1.38 (1.20–1.59) <0.001 1 1.10 (0.92–1.31) 1.09 (0.90–1.32) 1.12 (0.92–1.36) 1.06 (0.84–1.33) 0.624
Larynx
  No. cancer 759 913 874 1094 970 31 49 67 64 105
  HRa (99% CI) 1 0.98 (0.86–1.11) 0.93 (0.82–1.06) 0.98 (0.86–1.11) 0.93 (0.82–1.06) 0.247 1 1.12 (0.62–2.04) 1.46 (0.83–2.59) 0.91 (0.51–1.64) 1.24 (0.71–2.17) 0.733
  HRb (99% CI) 1 1.11 (0.97–1.27) 1.15 (1.00–1.33) 1.31 (1.12–1.53) 1.44 (1.20–1.73) <0.001 1 1.15 (0.63–2.11) 1.52 (0.83–2.78) 0.96 (0.50–1.85) 1.35 (0.65–2.79) 0.665
Oesophagus
  No. cancer 1522 1773 1484 1713 1475 92 132 128 190 237
  HRa (99% CI) 1 0.93 (0.85–1.02) 0.77 (0.70–0.85) 0.74 (0.67–0.81) 0.68 (0.61–0.75) <0.001 1 0.97 (0.68–1.38) 0.87 (0.61–1.24) 0.83 (0.59–1.16) 0.85 (0.61–1.19) 0.111
  HRb (99% CI) 1 1.17 (1.07–1.29) 1.12 (1.01–1.25) 1.25 (1.11–1.40) 1.47 (1.28–1.69) <0.001 1 1.04 (0.73–1.49) 0.98 (0.67–1.43) 0.98 (0.66–1.43) 1.10 (0.71–1.72) 0.751
Stomach
  No. cancer 13,544 17,782 18,213 21,898 20,122 4069 6402 6685 10,584 13,294
  HRa (99% CI) 1 1.04 (1.01–1.07) 1.06 (1.03–1.09) 1.07 (1.04–1.10) 1.09 (1.05–1.12) <0.001 1 1.13 (1.07–1.19) 1.14 (1.08–1.20) 1.20 (1.14–1.26) 1.28 (1.22–1.34) <0.001
  HRb (99% CI) 1 1.05 (1.02–1.08) 1.07 (1.04–1.11) 1.10 (1.06–1.14) 1.12 (1.08–1.17) <0.001 1 1.13 (1.07–1.19) 1.13 (1.07–1.20) 1.19 (1.12–1.26) 1.26 (1.18–1.34) <0.001
Colorectum
  No. cancer 10,803 15,809 17,189 21,888 21,324 6283 9785 10,577 15,846 19,893
  HRa (99% CI) 1 1.13 (1.10–1.17) 1.21 (1.18–1.25) 1.29 (1.25–1.33) 1.37 (1.33–1.41) <0.001 1 1.15 (1.10–1.19) 1.22 (1.17–1.27) 1.23 (1.18–1.28) 1.32 (1.27–1.37) <0.001
  HRb (99% CI) 1 1.14 (1.10–1.18) 1.22 (1.18–1.27) 1.31 (1.26–1.36) 1.40 (1.34–1.50) <0.001 1 1.12 (1.07–1.17) 1.17 (1.12–1.22) 1.15 (1.10–1.21) 1.19 (1.13–1.26) <0.001
Liverc
  No. cancer 5857 8458 8659 10,524 10,939 1410 2378 2731 4484 6233
  HRa (99% CI) 1 1.14 (1.10–1.20) 1.18 (1.13–1.23) 1.21 (1.16–1.26) 1.37 (1.31–1.43) <0.001 1 1.20 (1.10–1.31) 1.31 (1.21–1.43) 1.41 (1.30–1.53) 1.61 (1.49–1.75) <0.001
  HRb (99% CI) 1 1.15 (1.10–1.20) 1.18 (1.12–1.24) 1.21 (1.15–1.28) 1.38 (1.30–1.47) <0.001 1 1.11 (1.02–1.22) 1.16 (1.06–1.27) 1.18 (1.08–1.29) 1.22 (1.10–1.35) <0.001
Biliary tractd
  No. cancer 1354 1945 2218 2870 2768 584 1085 1392 2489 3476
  HRa (99% CI) 1 1.15 (1.05–1.26) 1.29 (1.18–1.41) 1.38 (1.27–1.51) 1.43 (1.31–1.56) <0.001 1 1.20 (1.05–1.37) 1.37 (1.21–1.56) 1.51 (1.33–1.70) 1.65 (1.46–1.86) <0.001
  HRb (99% CI) 1 1.12 (1.02–1.23) 1.22 (1.11–1.35) 1.29 (1.16–1.42) 1.28 (1.14–1.45) <0.001 1 1.12 (0.98–1.28) 1.22 (1.07–1.39) 1.27 (1.11–1.45) 1.25 (1.08–1.45) <0.001
Pancreas
  No. cancer 3712 5150 5573 6820 6381 1649 2759 2958 4798 6127
  HRa (99% CI) 1 1.08 (1.02–1.14) 1.15 (1.09–1.22) 1.18 (1.12–1.24) 1.19 (1.13–1.26) <0.001 1 1.17 (1.08–1.27) 1.19 (1.10–1.29) 1.25 (1.16–1.35) 1.30 (1.20–1.40) <0.001
  HRb (99% CI) 1 1.08 (1.02–1.15) 1.16 (1.09–1.23) 1.19 (1.11–1.27) 1.21 (1.12–1.31) <0.001 1 1.14 (1.05–1.24) 1.13 (1.04–1.23) 1.16 (1.06–1.26) 1.16 (1.05–1.27) 0.004
Lung
  No. cancer 8160 9501 9453 11,471 10,413 2173 3622 3894 6129 7317
  HRa (99% CI) 1 0.98 (0.94–1.01) 0.98 (0.94–1.02) 1.00 (0.96–1.04) 0.99 (0.95–1.03) 0.684 1 1.15 (1.07–1.23) 1.15 (1.08–1.24) 1.17 (1.09–1.25) 1.15 (1.08–1.23) <0.001
  HRb (99% CI) 1 1.09 (1.05–1.13) 1.17 (1.12–1.22) 1.29 (1.23–1.35) 1.44 (1.36–1.52) <0.001 1 1.15 (1.07–1.23) 1.16 (1.07–1.24) 1.17 (1.09–1.26) 1.16 (1.06–1.26) 0.001
Kidney
  No. cancer 1102 1990 2344 3110 3450 417 698 889 1339 1865
  HRa (99% CI) 1 1.36 (1.23–1.50) 1.58 (1.44–1.73) 1.75 (1.60–1.92) 2.10 (1.92–2.30) <0.001 1 1.28 (1.09–1.51) 1.63 (1.39–1.91) 1.65 (1.42–1.91) 1.91 (1.64–2.22) <0.001
  HRb (99% CI) 1 1.27 (1.15–1.40) 1.41 (1.28–1.57) 1.50 (1.35–1.67) 1.66 (1.47–1.88) <0.001 1 1.19 (1.01–1.40) 1.42 (1.21–1.68) 1.36 (1.15–1.60) 1.40 (1.16–1.69) <0.001
Bladder
  No. cancer 2135 3017 3159 4119 4208 259 489 572 939 1354
  HRa (99% CI) 1 1.14 (1.06–1.23) 1.18 (1.10–1.27) 1.28 (1.20–1.38) 1.41 (1.31–1.51) <0.001 1 1.20 (0.99–1.47) 1.22 (1.01–1.49) 1.21 (1.01–1.46) 1.33 (1.11–1.60) <0.001
  HRb (99% CI) 1 1.14 (1.05–1.23) 1.17 (1.08–1.27) 1.26 (1.16–1.37) 1.38 (1.25–1.52) <0.001 1 1.19 (0.97–1.45) 1.20 (0.98–1.47) 1.17 (0.96–1.43) 1.27 (1.01–1.59) 0.048
Thyroid
  No. cancer 3499 6097 6525 8096 7967 19,022 25,509 23,095 29,019 27,486
  HRa (99% CI) 1 1.45 (1.37–1.53) 1.71 (1.62–1.80) 1.94 (1.84–2.05) 2.27 (2.15–2.39) <0.001 1 1.38 (1.35–1.41) 1.57 (1.53–1.61) 1.64 (1.59–1.68) 1.62 (1.58–1.67) <0.001
  HRb (99% CI) 1 1.33 (1.25–1.40) 1.49 (1.40–1.58) 1.60 (1.50–1.70) 1.68 (1.56–1.81) <0.001 1 1.27 (1.23–1.30) 1.36 (1.32–1.39) 1.33 (1.29–1.37) 1.16 (1.12–1.21) <0.001
Brain
  No. cancer 783 1014 996 1215 1173 600 855 930 1345 1706
  HRa (99% CI) 1 1.03 (0.91–1.16) 1.04 (0.92–1.18) 1.10 (0.98–1.24) 1.22 (1.07–1.37) <0.001 1 1.13 (0.98–1.29) 1.27 (1.11–1.46) 1.29 (1.13–1.47) 1.45 (1.27–1.66) <0.001
  HRb (99% CI) 1 1.06 (0.93–1.20) 1.09 (0.95–1.25) 1.18 (1.02–1.36) 1.34 (1.12–1.60) <0.001 1 1.10 (0.96–1.27) 1.22 (1.05–1.14) 1.21 (1.04–1.41) 1.32 (1.11–1.58) <0.001
Malignant melanoma
  No. cancer 153 230 311 356 353 181 207 253 410 509
  HRa (99% CI) 1 1.16 (0.89–1.52) 1.55 (1.20–2.01) 1.50 (1.17–1.93) 1.64 (1.27–2.12) <0.001 1 0.79 (0.60–1.02) 0.89 (0.69–1.16) 0.93 (0.73–1.18) 0.95 (0.75–1.07) 0.426
  HRb (99% CI) 1 1.15 (0.87–1.51) 1.52 (1.15–2.01) 1.45 (1.01–1.95) 1.56 (1.10–2.21) <0.001 1 0.78 (0.59–1.01) 0.87 (0.67–1.15) 0.90 (0.69–1.18) 0.90 (0.66–1.23) 0.880
Lymphoma
  No. cancer 1213 1648 1716 2147 1995 690 1178 1134 1686 1918
  HRa (99% CI) 1 1.07 (0.97–1.18) 1.13 (1.03–1.25) 1.23 (1.12–1.35) 1.31 (1.19–1.44) <0.001 1 1.34 (1.19–1.52) 1.35 (1.19–1.53) 1.42 (1.25–1.60) 1.46 (1.29–1.66) <0.001
  HRb (99% CI) 1 1.05 (0.95–1.16) 1.10 (0.99–1.23) 1.18 (1.05–1.32) 1.23 (1.07–1.41) <0.001 1 1.31 (1.16–1.49) 1.30 (1.13–1.48) 1.34 (1.17–1.54) 1.35 (1.15–1.58) <0.001
Leukaemia
  No. cancer 755 1001 996 1203 1085 434 620 615 855 1012
  HRa (99% CI) 1 1.05 (0.92–1.18) 1.07 (0.95–1.22) 1.13 (1.00–1.28) 1.18 (1.04–1.34) <0.001 1 1.17 (0.99–1.38) 1.23 (1.04–1.45) 1.22 (1.04–1.44) 1.31 (1.11–1.54) <0.001
  HRb (99% CI) 1 1.03 (0.90–1.17) 1.04 (0.91–1.20) 1.09 (0.94–1.26) 1.11 (0.93–1.33) 0.087 1 1.11 (0.94–1.31) 1.12 (0.94–1.34) 1.07 (0.89–1.29) 1.06 (0.85–1.31) 0.899
Multiple myeloma
  No. cancer 405 517 576 707 679 176 304 403 653 796
  HRa (99% CI) 1 1.01 (0.85–1.20) 1.12 (0.95–1.33) 1.17 (0.99–1.37) 1.24 (1.05–1.47) <0.001 1 1.17 (0.92–1.49) 1.43 (1.13–1.82) 1.48 (1.19–1.86) 1.48 (1.18–1.86) <0.001
  HRb (99% CI) 1 0.95 (0.80–1.14) 1.01 (0.84–1.22) 1.01 (0.83–1.23) 1.00 (0.79–1.26) 0.701 1 1.14 (0.89–1.47) 1.38 (1.08–1.77) 1.40 (1.10–1.80) 1.36 (1.03–1.80) 0.003
Prostate
  No. cancer 6531 9295 10,375 13,359 12,932
  HRa (99% CI) 1 1.15 (1.10–1.20) 1.25 (1.20–1.30) 1.33 (1.27–1.38) 1.37 (1.32–1.43) <0.001 —
  HRb (99% CI) 1 1.09 (1.05–1.14) 1.16 (1.10–1.21) 1.19 (1.14–1.25) 1.17 (1.11–1.23) <0.001 —
Testis
  No. cancer 227 293 298 346 341
  HRa (99% CI) 1 1.01 (0.80–1.27) 1.07 (0.85–1.34) 1.08 (0.87–1.36) 1.23 (0.98–1.55) 0.010 —
  HRb (99% CI) 1 1.00 (0.79–1.27) 1.05 (0.82–1.36) 1.06 (0.81–1.40) 1.20 (0.86–1.67) 0.149 —
Breast-premenopausal
  No. cancer 8897 9958 7083 6775 4479
  HRa (99% CI) — 1 1.03 (0.99–1.08) 1.03 (0.99–1.08) 1.02 (0.98–1.07) 0.98 (0.93–1.03) 0.432
  HRb (99% CI) — 1 1.05 (1.01–1.09) 1.06 (1.02–1.11) 1.07 (1.02–1.13) 1.05 (0.98–1.13) 0.007
Breast-postmenopausal
  No. cancer 2942 5674 6778 10,365 11,884
  HRa (99% CI) — 1 1.00 (0.94–1.06) 1.04 (0.99–1.11) 1.08 (1.02–1.14) 1.15 (1.09–1.22) <0.001
  HRb (99% CI) — 1 0.95 (0.89–1.01) 0.96 (0.90–1.02) 0.95 (0.89–1.00) 0.93 (0.87–0.99) 0.035
Uterus-premenopausal
  No. cancer 692 795 580 684 824
  HRa (99% CI) — 1 1.11 (0.97–1.27) 1.15 (0.99–1.33) 1.39 (1.21–1.61) 2.31 (2.01–2.66) <0.001
  HRb (99% CI) — 1 0.95 (0.83–1.09) 0.87 (0.74–1.02) 0.93 (0.78–1.09) 1.13 (0.92–1.39) 0.546
Uterus-postmenopausal
  No. cancer 408 834 1024 1612 2273
  HRa (99% CI) — 1 1.05 (0.90–1.23) 1.13 (0.97–1.32) 1.19 (1.03–1.38) 1.55 (1.34–1.78) <0.001
  HRb (99% CI) — 1 0.96 (0.82–1.12) 0.95 (0.82–1.12) 0.93 (0.79–1.08) 1.02 (0.86–1.21) 0.695
Ovary-premenopausal
  No. cancer 1269 1261 886 882 799
  HRa (99% CI) — 1 0.99 (0.89–1.10) 1.00 (0.90–1.13) 1.05 (0.93–1.18) 1.35 (1.19–1.52) <0.001
  HRb (99% CI) — 1 0.97 (0.87–1.08) 0.97 (0.85–1.10) 0.99 (0.86–1.14) 1.23 (1.03–1.47) 0.072
Ovary-postmenopausal
  No. cancer 597 1102 1447 2164 2748
  HRa (99% CI) — 1 0.95 (0.83–1.08) 1.06 (0.93–1.20) 1.02 (0.90–1.15) 1.14 (1.01–1.28) <0.001
  HRb (99% CI) — 1 0.93 (0.81–1.06) 1.02 (0.89–1.16) 0.96 (0.84–1.09) 1.03 (0.89–1.20) 0.299
Cervix
  No. cancer 2459 2986 2693 3587 4117
  HRa (99% CI) — 1 1.08 (1.01–1.16) 1.11 (1.03–1.19) 1.13 (1.05–1.21) 1.24 (1.15–1.33) <0.001
  HRb (99% CI) — 1 1.05 (0.98–1.13) 1.05 (0.97–1.14) 1.05 (0.96–1.14) 1.10 (1.00–1.22) 0.051
Open in a new tab

HR hazard ratio, CI confidence interval.

aAdjusted for age, smoking status (3 categories), alcohol drinking (3 categories), physical activity (2 categories), co-morbidities (e.g., hypertension, type 2 diabetes, and hyperlipidemia; all are 2 categories, respectively), and socioeconomic status (2 categories).

bAdjusted for body mass index (continuous) additionally.

cIncludes intrahepatic biliary tract cancer.

dIncludes gallbladder cancer and extrahepatic biliary tract cancer

Fig. 1.

Fig. 1

Open in a new tab

Forest plot of the HR for each cancer across WC quintiles before and after BMI adjustment, from models (adjusted for age, smoking status (3 categories), alcohol drinking (3 categories), physical activity (2 categories), co-morbidities (e.g., hypertension, type 2 diabetes, and hyperlipidemia; all are 2 categories, respectively), and socioeconomic status (2 categories)) fitted as a linear effect. a Male. b Female. HR hazard ratio, WC waist circumference, BMI body mass index

Selected sensitivity analyses are shown in the supplementary tables. Because smoking has an inverse relationship with obesity and is a well-known risk factor for many cancers, we analysed data for those who had never smoked. The overall trend and its significance involving the WC-cancer association remained in smoking-related cancer, while the significance diminished in some cancers (Table S1). To examine the effect of preclinical cancers that may cause weight loss and thus bias the association between obesity and cancer, we repeated the analyses after excluding cancer occurring in patients within the first 2 years of follow up, but found no significant difference (Table S2).

Discussion

Continuous updates of the scientific literature have supported the association between high WC and the risk of cancer. In a single dataset, we determined the associations between WC and the incidence of cancer, when considering potential confounders including the BMI. To the best of our knowledge, this is the largest study to estimate the effects of WC-cancer associations across a range of sites. In addition, studies on this issue have been conducted primarily in Western populations. Asians, including Koreans, tend to have relatively small body frames and condensed body fat. Compared with Caucasians, they have a higher body fat percentage for a given BMI29. Moreover, they have higher amounts of abdominal adipose tissue31,32 and lower muscle mass for a given BMI, leading to a greater tendency toward central obesity and to more susceptible to insulin resistance33,34. Thus, the effects of abdominal obesity on the development of cancer in Asians might differ from those in Caucasians.

Several mechanisms have been proposed to explain links between adiposity and increased cancer risk involving sex hormone metabolism, insulin and IFG signalling, and adipokine pathophysiology35. However, excess body fat is a heterogeneous condition in which individuals with similar BMIs may have distinct cancer risks. Our results provide a potential explanation for the risk differential that persists after accounting for BMI. Few studies have conducted further adjustments between the central adiposity index and BMI to clarify their independent roles in the risk of cancers.

It is currently recognised that a proportion of obese individuals might not be at an increased risk for metabolic complications of obesity. Recent studies have suggested a correlation between metabolic health and cancer outcomes36,37. Metabolically active visceral fat releases substantial amounts of growth factors, inflammatory markers, free fatty acids, and locally produced oestrogen and adipokines, which might contribute to the development of cancer38. However, WC not only distinguishes lean from adipose tissue, but reflects adipose tissues in both subcutaneous and visceral areas, which obscure any separate roles of compartments in determining the cancer risk. The most comprehensive quantitative modalities will come from large-scale imaging (i.e., abdominal fat computed tomography and dual-energy X-ray absorptiometry) projects, which should result in the identification of dominant mechanistic pathways.

We identified several cancers related to central obesity that were independent of general adiposity. Overall, this finding was consistent with those from recent studies of cancers of the gastrointestinal tract10,39,40, lung14, hepato-pancreato-biliary system13,41–43, head and neck17, and kidney40. Cancers of the thyroid16, prostate20,44, bladder45, and skin46, which had conflicting or non-significant results, also had significantly positive dose-dependent relationships with increasing WC. Non-significant results in prior studies might be simply due to relatively small sample sizes, as their associations were similar to ours, and the associations reached significance before adjusting for the BMI. However, for prostate cancer, we were unable to explain the disagreement with previous results, because the association between body adiposity and incident prostate cancer depends mainly on the stage or subtype of prostate cancer20. Prostate cancer has a large clinical heterogeneity, which ranges from microscopic, well-differentiated indolent tumours to aggressive and lethal diseases. However, in the present study, we could not obtain this detailed clinical information.

We did find a new association between WC and lymphoma/brain tumours, which has not been previously reported because the BMI was not adjusted in previous studies47,48. In contrast, we could not find an association for leukaemia that has previously been reported to be associated with central adiposity49. For multiple myeloma, while a recent Mendelian randomisation study reported a non-significant (p = 0.06) reverse association of the BMI-adjusted WC50, we found a positive association in female (Ptrend = 0.003). Given the largely unknown etiology and the heterogeneous entity of these malignancies, further studies are warranted to clarify their associations with central adiposity.

We found differential associations of WC by sex with the risks of some cancers, but this might have been largely because of the differences in sample size (i.e., cancers of larynx, oesophagus, and bladder). Besides the sample size gap, a male-specific association with malignant melanoma was also found. Malignant melanoma is the most aggressive form of skin cancer, which is considered the fastest growing cancer51. Overall, a link between malignant melanoma and general adiposity remains unclear52, although a recent meta-analysis reported a male-specific effect53. To the best our knowledge, only a single study investigated the impact of central obesity when considering the BMI, with no convincing associations reported46. A possible explanation is that increased body surface in males may simply denote a larger surface at risk for sunlight exposure, thus providing an increased association with the incidences of melanoma53. However, our sensitivity analyses showed that the positive association between WC and melanoma in males was largely affected by the smoking status, and not by the BMI. Smoking may be at least a strong modifier in the WC-melanoma associations, even though it is not a risk factor for melanoma54. There has also been growing evidence that smoking is associated with abdominal fat accumulation55,56.

In the present study, positive associations were mostly attenuated after BMI adjustment; however, notable reversions toward positive associations were found in cancers of the oral cavity, larynx, oesophagus, and lung. While the mean BMI values are generally lower in smokers than in non-smokers57, cigarette smoking has been positively associated with central adiposity55,58. Our additional analyses in non-smokers revealed an unaltered direction or significance of association. This means that metabolic derangements, represented by central adiposity, are still responsible for increased risk in such cancers, even when excluding the effect of smoking. In this regard, obtaining a WC measurement together with BMI may provide essential information that might not be feasible when assessing each parameter separately.

In a similar manner, it was noteworthy that among oestrogen-driven cancers, BMI-adjusted WC predicted the risk of premenopausal breast cancer. Before menopause, plasma levels of oestrogen were not directly related to general adiposity, and obese premenopausal females had lower estradiol levels because they are more likely to have anovulatory cycles59. Thus, the positive association with WC in this study cannot be explained by the sex hormone hypothesis, which was supported by the finding that central obesity was associated with an increased risk of oestrogen receptor-negative breast cancer in premenopausal women60. Central obesity is a well-known indicator of hyperinsulinemia and higher levels of IGF-1 that are related to premenopausal breast cancer risk61. Overall, our results indicated that metabolic conditions may be more important than hormonal mechanisms in premenopausal breast cancer.

Occult cancers are an important type of cancer to be considered. To eliminate bias, we performed sensitivity analyses excluding an incident cancer within the initial 2 years of follow up; however, we could not find any significant differences. We assumed that this was because many of the occult cancer patients were diagnosed and excluded through their health status examination near the baseline. In addition, the general perception is that obesity does not initiate cancer, but rather promotes cancer in clinical presentations over several years. The precise time lag between development and duration of obesity and the occurrence of cancer is still not well established.

There are some limitations in this study. Our study was confined to individuals who took a health examination, so those without WC data were excluded, introducing the possibility of selection bias. Based on the 2009 Korea National Health and Nutrition Examination Survey data62, a prevalence of obesity/smoker was 35.8%/47% for male and 26%/7.1% for female, respectively: these are comparable to our results (37.3%/44.9% for male and 26.6%/4.1% for female). Moreover, our main conclusions are unlikely to have been seriously affected, because we investigated the WC-cancer relation itself. A second limitation was the potential for residual confounding that we could not consider. Our outcome data were insufficiently detailed to explore potential differences between cancer subtypes, in particular for oesophageal cancer6, lung cancer63, breast cancer64, prostate cancer20, lymphoma65, and colon cancer66. We also had no detailed information on critical risk factors for some cancers, such as female reproductive factors for oestrogen-dependent tumours, viral hepatitis information for liver cancer, ultraviolet exposures for melanoma, and amounts or forms of tobacco consumption for lung cancer. Mendelian randomisation may offer a solution to the problem of residual confounding, under certain conditions67. A final limitation was the assumption of an unchangeable obesity index. We assessed only baseline measures, so these single measurements may not have reflected changes that occurred during the follow up. Previous studies68,69 also emphasised the importance of time duration with a high BMI and its association with cancer development, in a similar manner to other diseases70. Longer follow up with sequential measurements is needed to more fully support a causal association.

Despite these limitations, our study has methodological strengths. As a longitudinal, nationwide, population-based cohort study, the size of the dataset was close to the entire population of adult Koreans. Compulsory health examinations also allowed us to use direct anthropometric measurements, which are preferred over self-reported data71. To date, no other study has used direct measurements of WC in such a large sample size for a wide range of cancer sites. Our results provide evidence on the independent role of central obesity as a predictor for cancer incidence, even in a relatively lean Asian population. However, this result is only indirect evidence that the interventional effect of WC reduction will decrease the risk of cancer. Heterogeneity in the effects of BMI also suggests that there are different mechanisms or combinations of mechanisms associated with different sites. Integration of experimental research on underlying mechanisms linking central obesity to the identified cancers in our study is necessary to confirm our conclusions.

Electronic supplementary material

Supplemental tables 1-2 (54.8KB, docx)

Acknowledgements

We would like to thank the Korean National Health Insurance Corporation and all the participants of the study and health check-up. This work was partially supported by the Korean Society for the Study of Obesity, which had not role in study design, data collection, data analysis, data interpretation, or writing of the report.

Author contributions

All authors take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: K.R.L., M.H.S. and I.C.H. Acquisition and analysis of data: K.D.H. and J.J. Interpretation of data: All authors. Drafting of the manuscript: K.R.L., M.H.S., and I.C.H. Critical revision of the manuscript and approval of final version: All authors. K.R.L. and M.H.S. contributed equally as a first author.

Ethics approval and consent to participate

The Institutional Review Board of Gachon University Gil Medical Center (No. GCIRB2017-277) and of the Korean National Institute for Bioethics Policy (No. P01-201603-21-005) approved the study. The ethic committee waived the requirement for informed participant consent. This study was performed in accordance with the Declaration of Helsinki.

Competing interests

The authors declare no competing interests.

Availability of data and material

The data were provided by National Health Insurance Sharing Service. These are third party data. We did not have any special access privileges that others would not have. Anyone can submit a research proposal online (https://nhiss.nhis.or.kr/bd/ab/bdaba021eng.do). If the proposal is accepted by an evaluation committee of NHIC, researcher would access the de-identified NHIC dataset after paying some fee. Permission for the use of health check-up data was granted by the NHIC (NHIS-2018-1-174).

Footnotes

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Supplementary information is available for this paper at 10.1038/s41416-018-0214-7.

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