Abstract
Background Although several studies have evaluated the relationship between adult height and mortality, their results have not been entirely consistent. Little is known about components of adult height in relation to mortality, particularly in developing countries.
Methods We examined the association of adult height and its components (leg and trunk length) with mortality using data from 74 869 Chinese women and 61 333 men in the Shanghai Women's (1996–2008) and Men's (2002–2008) Health Studies. Anthropometric measurements, including standing and sitting height and weight, were taken at baseline by trained interviewers according to a standard protocol. Deaths were ascertained by biennial home visits and linkage with the vital statistics registry. Cox regression models were used to evaluate the associations.
Results Neither height nor its components were associated with all-cause mortality. Height and, less consistently, its components were positively associated with cancer mortality, but inversely associated with cardiovascular disease (CVD) mortality. Hazard ratios (HRs) [95% confidence intervals (CIs)] for cancer mortality per 1-SD increment in height, trunk and leg length were 1.06 (1.01–1.12), 1.07 (1.01–1.12) and 1.03 (0.98–1.08), respectively, in women, and 1.13 (1.05–1.22), 1.09 (1.00–1.19) and 1.10 (1.03–1.16), respectively, in men. The corresponding HRs for CVD mortality were 0.89 (0.84–0.95), 0.93 (0.87–0.99) and 0.91 (0.86–0.98) in women, and 0.93 (0.86–1.02), 0.89 (0.81–0.98) and 0.99 (0.92–1.06) in men.
Conclusions Our results suggest that different mechanisms may be involved in linking height and its components with cancer and CVD mortality.
Keywords: Body height, cancer, cardiovascular disease, Chinese, cohort study, mortality
Introduction
It has been suggested that poor nutrition, illness and adverse living circumstances experienced during early life may contribute to the development of chronic disease in later life.1,2 Attained adult height is known to be influenced by these early life factors and has been widely used as a surrogate measure for early life exposures in epidemiologic studies. Short adult stature has been previously linked to increased risk of cardiovascular disease (CVD), but decreased risk of cancer, although the results have not been entirely consistent.3–6 Potential mechanisms underlying the opposing associations of adult height with CVD and cancer are not fully understood. Elucidating components of adult height (leg and trunk length) in relation to disease risk may provide some insight into the relationship between early life exposures, height and adult health. It has been suggested that leg length is a sensitive marker of nutritional status and other exposures in childhood, and may be the component of adult height that is related to disease risk. Relatively few prospective studies,7–10 however, have examined the associations of leg length and trunk length with disease risk. Furthermore, studies of adult height and disease risk published to date have been conducted largely in affluent Western countries,3,8,11–16 and data from less-affluent developing countries are sparse;17–19 it is unclear whether the associations of adult height and its components with health outcomes differ by the populations under study.
We evaluated the associations of adult height, leg length and trunk length with risk of all-cause and cause-specific mortality in two large cohort studies conducted in China, the Shanghai Women's Health Study (SWHS) and the Shanghai Men's Health Study (SMHS).
Methods
Study population
The SWHS and the SMHS are both population-based, prospective cohort studies. Details of the study methodologies have been reported previously.20,21 Participants were identified through the Shanghai Resident Registry, which kept personal identification cards for all permanent residents in urban Shanghai. All women aged 40–70 years and men aged 40–74 years living in the study communities were approached for participation by trained interviewers and local community health workers. From 1996 to 2000, the SWHS recruited 74 942 women and from 2002 to 2006, the SMHS enrolled 61 500 men. The participation rates were 92.7% for SWHS and 74.1% for SMHS. At baseline, each subject completed an in-person interview to gather information on demographics, lifestyle habits, dietary intake, physical activity, occupational history, and menstrual and reproductive history (for women only). Anthropometric measurements were also taken for each subject at baseline. The study protocols were approved by the relevant institutional review boards of all institutes involved and written informed consent was obtained from all participants.
Anthropometry
Anthropometric measurements, including standing height (cm), sitting height (cm), weight (kg), and circumferences of the waist and hips (cm), were taken by trained health professionals according to a standard protocol. Each parameter was measured twice with a tolerance error of 1 cm for height and circumference measurements, and 1 kg for the weight measurement. If the difference between two measurements was greater than tolerance, a third measurement was then taken. All parameters were estimated as the mean value of the two closest measurements. The standing height was measured to the nearest 0.1 cm without shoes. The sitting height (trunk length) was measured to the nearest 0.1 cm with the participant seated on a stool against the wall. Leg length was calculated as the difference between standing height and trunk length. Weight was measured to the nearest 0.1 kg using a digital weight scale that was calibrated every 6 months. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in metres. Waist circumference was taken at 2.5 cm above the umbilicus and hip circumference at the level of maximum protrusion of the gluteal muscles. Waist–hip ratio (WHR) was then calculated as waist circumference divided by hip circumference.
Outcome ascertainment
All participants were followed-up for major chronic diseases and vital status through home visits conducted every 2 years and record linkage to the Shanghai Cancer Registry and the Shanghai Vital Statistics Registry. Information on newly diagnosed cancer cases in cohort members was obtained from the Shanghai Cancer Registry. For the SWHS, the in-person follow-up rates for the first (2000–2002), second (2002–2004) and third (2004–2007) surveys were 99.8, 98.7 and 95.0%, respectively. For the SMHS, the response rate for the first follow-up (2004–2008) was 97.6%. There were 34 cohort members who moved out of the study area and for whom vital status could not be determined via record linkage to the vital statistics registry; these subjects were treated as censored observations. The primary endpoint for this analysis was death from any cause that occurred after the baseline survey but before 31 December 2008. The underlying cause of death was determined on the basis of death certificates and coded according to the codes of the International Classification of Disease, 9th Revision (ICD-9). In addition to total mortality, we also examined deaths from cancer (ICD-9 codes 140–208) and CVD (ICD-9 codes 390–459). We further divided cancer deaths into deaths from female cancers, including breast (ICD-9 code 174), uterine (179, 182), cervical (180) and ovarian (183) cancers, and other cancer deaths for women, and smoking-related cancers, including cancers of the lip, oral cavity, pharynx (ICD-9 codes 140–149), oesophagus (150), pancreas (157), respiratory tract (160–163) and urinary tract (188–189), and smoking-unrelated cancers for men. We also examined deaths from stroke (ICD-9 codes 430–438), ischaemic heart disease (ICD codes 410–414), and other circulatory diseases in women and in men.
Statistical analysis
Participants with missing data on anthropometric measurements (65 women and 167 men) or who were lost to follow-up shortly after the study enrolment (8 women) were excluded, leaving 74 869 women and 61 333 men for analyses. Participants were categorized by sex-specific quintiles of height, leg length and trunk length, and the lowest quintiles were used as the reference categories. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated using Cox proportional hazards models with age as the time metric. Entry time was defined as the age at enrolment and exit time was defined as the age at death or last date of follow-up, whichever came first. Birth calendar year (eight categories: 1926–1932, 1933–1937, 1938–1941, 1942–1945, 1946–1949, 1950–1953, 1954–1957 and after 1958) was treated as a strata variable in the Cox models. Multivariable models were further adjusted for education (four categories: up to elementary school, middle school, high school, beyond college), occupation (three categories: professional, clerical and manual workers), income (three categories: for men: monthly per capita income <1000 yuan, 1000–2000 yuan, and >2000 yuan; for women: annual family income <20 000, 20 000–30 000 yuan and >30 000 yuan), BMI (quintiles), WHR (quintiles), total physical activity [measured in metabolic equivalent (MET) hours/week, quintiles], cigarette smoking (never, former, current: 1–9, 10–19, ≥20 cigarettes/day for men; never/ever for women), alcohol consumption (0, 0.1–1.0, 1.1–2.0 and >2.0 drinks/day for men; never/ever for women), daily intakes of total energy, red meat, fruit and vegetables (continuous), and menopausal status (pre-/post-menopausal for women). We also calculated HRs of mortality for per 1-standard deviation (SD) increment in height, trunk length and leg length. All analyses were performed by using SAS, version 9.1 software (SAS Institute, Inc., Cary, NC, USA), and all tests of statistical significance were based on two-sided probability.
Results
The average age and height of the study participants were 52.1 years and 157.5 cm for women and 54.9 years and 169.8 cm for men, respectively. Table 1 shows the age-adjusted baseline characteristics of participants according to height quintiles. Among both women and men, taller participants were younger and had a higher level of socioeconomic status (SES) as measured by education level, occupation and family income, as compared with shorter participants. They also appeared to have higher intakes of total energy, red meat, fruit and vegetables, higher WHR, and a lower level of physical activity, and were more likely to smoke cigarettes. There was little correlation between the number of cigarettes smoked and height among current smokers (r = 0.018) in men. Taller men were also more likely to drink alcohol than shorter men.
Table 1.
Baseline characteristics of participants according to quintiles of height,a the SWHS and SMHS
| Quintiles of height |
|||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | |
| Women (SWHS) | |||||
| Height (cm) | <153.0 | 153.0–155.8 | 156.0–158.9 | 159.0–161.9 | ≥162.0 |
| No. of subjects | 13 825 | 12 652 | 16 415 | 14 385 | 17 592 |
| Age at baseline (years) | 58.2 ± 8.9 | 54.8 ± 9.1 | 52.6 ± 8.9 | 50.5 ± 8.2 | 48.4 ± 7.1 |
| Low incomeb (%) | 62.2 | 57.3 | 54.4 | 51.3 | 51.4 |
| ≤Middle school (%) | 67.7 | 62.1 | 58.4 | 55.8 | 52.0 |
| Manual workers (%) | 55.9 | 52.6 | 50.2 | 48.8 | 45.9 |
| Ever smoked (%) | 2.2 | 2.7 | 2.9 | 3.2 | 3.6 |
| Ever drank (%) | 2.3 | 2.2 | 2.1 | 2.3 | 2.4 |
| Post-menopausal (%) | 50.3 | 49.8 | 49.4 | 49.4 | 48.3 |
| BMI (kg/m2) | 24.3 ± 3.5 | 24.1 ± 3.4 | 24.0 ± 3.3 | 23.9 ± 3.4 | 23.8 ± 3.3 |
| WHR | 0.8 ± 0.1 | 0.8 ± 0.1 | 0.8 ± 0.1 | 0.8 ± 0.1 | 0.8 ± 0.1 |
| MET-hours/week | 110.1 ± 46.9 | 108.7 ± 45.4 | 106.8 ± 45.1 | 105.2 ± 45.4 | 102.7 ± 46.4 |
| Daily intake | |||||
| Energy (kcal/day) | 1617.5 ± 419.0 | 1649.5 ± 405.4 | 1676.7 ± 403.0 | 1694.1 ± 405.4 | 1726.2 ± 414.9 |
| Red meat (g/day) | 47.9 ± 37.6 | 50.3 ± 36.4 | 51.3 ± 36.2 | 51.5 ± 36.4 | 53.0 ± 37.2 |
| Fruit (g/day) | 238.4 ± 182.8 | 257.4 ± 176.8 | 265.2 ± 175.8 | 273.3 ± 176.8 | 282.8 ± 181.0 |
| Vegetables (g/day) | 280.9 ± 176.9 | 290.9 ± 171.1 | 295.2 ± 170.1 | 303.0 ± 171.1 | 309.4 ± 175.1 |
| Men (SMHS) | |||||
| Height (cm) | <165.0 | 165.0–168.4 | 168.5–170.9 | 171.0–174.9 | ≥175.0 |
| No. of subjects | 11 148 | 13 162 | 10 143 | 14 358 | 12 522 |
| Age at baseline (y) | 60.6 ± 9.9 | 57.2 ± 9.8 | 54.9 ± 9.4 | 53.5 ± 8.9 | 51.2 ± 8.0 |
| Low incomeb (%) | 63.2 | 57.5 | 55.2 | 52.8 | 49.2 |
| ≤Middle school (%) | 48.9 | 43.9 | 40.6 | 38.8 | 34.2 |
| Manual workers (%) | 56.6 | 53.3 | 50.5 | 50.2 | 46.0 |
| Ever smoked (%) | 66.5 | 69.5 | 69.9 | 70.4 | 70.8 |
| Ever drank (%) | 33.2 | 33.8 | 33.7 | 33.5 | 34.7 |
| BMI (kg/m2) | 23.8 ± 3.2 | 23.7 ± 3.1 | 23.7 ± 3.1 | 23.7 ± 3.1 | 23.7 ± 3.1 |
| WHR | 0.8 ± 0.1 | 0.8 ± 0.1 | 0.8 ± 0.1 | 0.8 ± 0.1 | 0.8 ± 0.1 |
| MET-hours/week | 62.4 ± 34.1 | 61.0 ± 33.2 | 59.7 ± 33.1 | 58.5 ± 33.2 | 57.2 ± 33.8 |
| Daily intake | |||||
| Energy (kcal/day) | 1855.8 ± 494.5 | 1884.2 ± 482.4 | 1901.4 ± 480.5 | 1928.9 ± 482.5 | 1966.3 ± 490.3 |
| Red meat (g/day) | 60.9 ± 45.3 | 61.9 ± 44.2 | 63.2 ± 44.0 | 63.7 ± 44.2 | 66.3 ± 44.9 |
| Fruit (g/day) | 139.2 ± 129.6 | 148.3 ± 126.4 | 150.9 ± 125.9 | 157.5 ± 126.5 | 160.1 ± 128.5 |
| Vegetables (g/day) | 330.3 ± 198.2 | 337.8 ± 193.3 | 344.5 ± 192.5 | 348.8 ± 193.4 | 356.3 ± 196.5 |
aFor means: least-square means estimated through linear regression models, which included age as independent variable; for percentage: directly adjusted for age in 5-year categories; all tests for trend were significant (P < 0.05) expect for women and men who ever drank, and BMI for men.
bFor men: monthly per capita income <1000 Chinese Yuan; for women: annual family income <20 000 Chinese Yuan.
The correlation coefficients of age with height, leg length and trunk length in women were −0.38, −0.22 and −0.37, respectively. The corresponding coefficients in men were −0.33, −0.22 and −0.32.
In the SWHS cohort, we identified 3624 deaths, including 1018 from CVD [55.8% stroke, 21.8% coronary heart disease (CHD) and 22.4% other circulatory diseases] and 1653 from cancer (18.9% lung cancer, 14.3% colorectal cancer, 12.5% stomach cancer, 7.3% liver cancer, 10.1% breast cancer and 36.9% other cancers). In the SMHS cohort, we identified 1860 deaths, including 602 from CVD (49.0% stroke, 34.1% CHD and 16.9% other circulatory diseases) and 808 from cancer (30.9% lung cancer, 14.8% liver cancer, 13.0% stomach cancer, 7.7% colorectal cancer and 33.6% other cancers). Tables 2 and 3 show age- and multivariate-adjusted HRs of total and cause-specific mortality by quintiles of adult height in women and men. After adjustment for potential confounding factors, no significant association was found between height and total mortality in either women or men. However, greater height was related to significantly elevated risk of cancer mortality in both sexes. The multivariable-adjusted HRs (95% CIs) of cancer mortality for per 1-SD increment in height were 1.06 (1.01–1.12) for women and 1.13 (1.05–1.22) for men. In contrast, an inverse association between height and CVD mortality was found both in women and in men, with corresponding HRs of 0.89 (0.84–0.95) and 0.93 (0.86–1.02). The inverse association of height with CVD mortality was more evident for stroke than for ischaemic heart disease in both sexes.
Table 2.
HRs and 95% CIs of total and cause-specific mortality according to quintiles of height,a the SWHS
| Quintiles of height (cm) |
||||||
|---|---|---|---|---|---|---|
| <153.0 | 153.0–155.8 | 156.0–158.9 | 159.0–161.9 | ≥162.0 | Per 1-SD increase | |
| All causes | ||||||
| No. of deaths | 1154 | 753 | 731 | 514 | 472 | |
| Model 1b | 1.00 | 0.95 (0.87–1.04) | 0.88 (0.80–0.96) | 0.89 (0.80–0.99) | 0.90 (0.80–1.01) | 0.94 (0.91–0.98) |
| Model 2c | 1.00 | 0.99 (0.90–1.08) | 0.93 (0.84–1.02) | 0.95 (0.85–1.05) | 0.97 (0.86–1.08) | 0.97 (0.94–1.01) |
| Cancer | ||||||
| No. of deaths | 431 | 324 | 372 | 254 | 272 | |
| Model 1 | 1.00 | 1.04 (0.90–1.21) | 1.10 (0.95–1.27) | 1.04 (0.89–1.22) | 1.16 (0.98–1.36) | 1.05 (1.00–1.10) |
| Model 2 | 1.00 | 1.06 (0.92–1.23) | 1.13 (0.98–1.30) | 1.08 (0.92–1.26) | 1.21 (1.03–1.42) | 1.06 (1.01–1.12) |
| Female cancers | ||||||
| No. of deaths | 52 | 53 | 58 | 48 | 73 | |
| Model 1 | 1.00 | 1.33 (0.90–1.95) | 1.27 (0.87–1.85) | 1.36 (0.91–2.04) | 1.98 (1.35–2.90) | 1.21 (1.06–1.37) |
| Model 2 | 1.00 | 1.32 (0.90–1.95) | 1.26 (0.86–1.85) | 1.37 (0.91–2.05) | 1.98 (1.35–2.90) | 1.21 (1.06–1.37) |
| Other cancers | ||||||
| No. of deaths | 379 | 271 | 314 | 206 | 199 | |
| Model 1 | 1.00 | 1.01 (0.86–1.18) | 1.08 (0.93–1.26) | 1.00 (0.84–1.19) | 1.02 (0.85–1.22) | 1.02 (0.96–1.08) |
| Model 2 | 1.00 | 1.02 (0.87–1.19) | 1.11 (0.95–1.29) | 1.03 (0.86–1.23) | 1.07 (0.89–1.28) | 1.04 (0.98–1.10) |
| CVD | ||||||
| No. of deaths | 387 | 215 | 187 | 139 | 90 | |
| Model 1 | 1.00 | 0.86 (0.73–1.02) | 0.75 (0.63–0.89) | 0.85 (0.70–1.04) | 0.67 (0.53–0.85) | 0.86 (0.80–0.92) |
| Model 2 | 1.00 | 0.90 (0.76–1.07) | 0.80 (0.67–0.95) | 0.92 (0.76–1.12) | 0.74 (0.58–0.94) | 0.89 (0.84–0.95) |
| Stroke | ||||||
| No. of deaths | 208 | 129 | 105 | 82 | 44 | |
| Model 1 | 1.00 | 0.96 (0.77–1.19) | 0.77 (0.61–0.98) | 0.93 (0.72–1.21) | 0.61 (0.44–0.86) | 0.86 (0.79–0.94) |
| Model 2 | 1.00 | 1.01 (0.81–1.26) | 0.83 (0.66–1.06) | 1.01 (0.77–1.31) | 0.66 (0.47–0.93) | 0.89 (0.82–0.97) |
| Ischaemic heart disease | ||||||
| No. of deaths | 90 | 42 | 40 | 25 | 25 | |
| Model 1 | 1.00 | 0.74 (0.51–1.06) | 0.71 (0.49–1.04) | 0.70 (0.45–1.10) | 0.91 (0.57–1.43) | 0.88 (0.77–1.01) |
| Model 2 | 1.00 | 0.77 (0.53–1.12) | 0.77 (0.53–1.12) | 0.77 (0.49–1.20) | 1.01 (0.64–1.61) | 0.92 (0.80–1.06) |
| Other circulatory diseases | ||||||
| No. of deaths | 89 | 44 | 42 | 32 | 21 | |
| Model 1 | 1.00 | 0.75 (0.52–1.07) | 0.70 (0.48–1.01) | 0.80 (0.53–1.21) | 0.63 (0.38–1.03) | 0.83 (0.73–0.96) |
| Model 2 | 1.00 | 0.79 (0.55–1.13) | 0.74 (0.51–1.07) | 0.86 (0.57–1.30) | 0.67 (0.41–1.10) | 0.87 (0.76–0.99) |
| Causes other than cancer or CVD | ||||||
| No. of deaths | 336 | 214 | 172 | 121 | 110 | |
| Model 1 | 1.00 | 0.94 (0.79–1.11) | 0.72 (0.60–0.87) | 0.74 (0.60–0.92) | 0.77 (0.61–0.96) | 0.87 (0.81–0.93) |
| Model 2 | 1.00 | 1.00 (0.84–1.18) | 0.79 (0.65–0.95) | 0.82 (0.66–1.01) | 0.83 (0.66–1.05) | 0.91 (0.85–0.98) |
aThe total follow-up time for women in each quintile of height was 136 461, 126 248, 165 257, 145 366 and 178 132 person-years, respectively.
bModel 1: age adjusted only.
cModel 2: multivariate model: adjusted for birth calendar year, age, education, income, occupation, BMI, WHR, cigarette smoking, alcohol consumption, total physical activity (MET hours/week), daily intake of energy, red meat, fruit and vegetables and menopause status.
Table 3.
HRs and 95% CIs of total and cause-specific mortality according to quintiles of heighta, the SMHS
| Quintiles of height (cm) |
||||||
|---|---|---|---|---|---|---|
| <165.0 | 165.0–168.4 | 168.5–170.9 | 171.0–174.9 | ≥175.0 | Per 1-SD increase | |
| All causes | ||||||
| No. of deaths | 560 | 436 | 285 | 342 | 237 | |
| Model 1b | 1.00 | 0.88 (0.78–1.00) | 0.93 (0.81–1.08) | 0.92 (0.80–1.06) | 0.98 (0.84–1.15) | 0.97 (0.92–1.02) |
| Model 2c | 1.00 | 0.93 (0.82–1.05) | 1.00 (0.86–1.15) | 0.99 (0.86–1.14) | 1.06 (0.90–1.25) | 1.00 (0.95–1.05) |
| Cancer | ||||||
| No. of deaths | 206 | 198 | 113 | 164 | 127 | |
| Model 1 | 1.00 | 1.08 (0.89–1.31) | 0.99 (0.78–1.25) | 1.17 (0.95–1.45) | 1.38 (1.09–1.74) | 1.10 (1.02–1.18) |
| Model 2 | 1.00 | 1.11 (0.91–1.35) | 1.03 (0.82–1.30) | 1.24 (1.00–1.53) | 1.47 (1.16–1.86) | 1.13 (1.05–1.22) |
| Smoking-related cancers | ||||||
| No. of deaths | 92 | 94 | 58 | 76 | 62 | |
| Model 1 | 1.00 | 1.19 (0.89–1.59) | 1.22 (0.87–1.69) | 1.33 (0.98–1.82) | 1.74 (1.24–2.43) | 1.16 (1.04–1.29) |
| Model 2 | 1.00 | 1.22 (0.91–1.62) | 1.26 (0.90–1.76) | 1.40 (1.02–1.92) | 1.86 (1.32–2.62) | 1.18 (1.06–1.32) |
| Other cancers | ||||||
| No. of deaths | 114 | 104 | 55 | 88 | 65 | |
| Model 1 | 1.00 | 0.99 (0.76–1.29) | 0.82 (0.59–1.13) | 1.05 (0.79–1.39) | 1.12 (0.81–1.55) | 1.05 (0.95–1.16) |
| Model 2 | 1.00 | 1.02 (0.78–1.33) | 0.86 (0.62–1.19) | 1.10 (0.83–1.48) | 1.20 (0.87–1.67) | 1.08 (0.98–1.20) |
| CVD | ||||||
| No. of deaths | 203 | 136 | 100 | 104 | 59 | |
| Model 1 | 1.00 | 0.79 (0.63–0.98) | 0.96 (0.75–1.22) | 0.84 (0.66–1.07) | 0.77 (0.57–1.04) | 0.90 (0.83–0.98) |
| Model 2 | 1.00 | 0.84 (0.67–1.04) | 1.04 (0.82–1.33) | 0.89 (0.70–1.14) | 0.84 (0.62–1.14) | 0.93 (0.86–1.02) |
| Stroke | ||||||
| No. of deaths | 104 | 66 | 50 | 52 | 23 | |
| Model 1 | 1.00 | 0.72 (0.53–0.99) | 0.89 (0.63–1.25) | 0.76 (0.54–1.07) | 0.52 (0.33–0.84) | 0.84 (0.74–0.94) |
| Model 2 | 1.00 | 0.79 (0.58–1.08) | 1.00 (0.71–1.41) | 0.84 (0.60–1.19) | 0.59 (0.37–0.95) | 0.88 (0.78–1.00) |
| Ischaemic heart disease | ||||||
| No. of deaths | 63 | 53 | 35 | 34 | 20 | |
| Model 1 | 1.00 | 1.02 (0.71–1.47) | 1.16 (0.77–1.77) | 0.98 (0.64–1.49) | 0.98 (0.59–1.65) | 0.97 (0.84–1.12) |
| Model 2 | 1.00 | 1.05 (0.73–1.52) | 1.20 (0.79–1.83) | 0.98 (0.64–1.51) | 1.01 (0.60–1.72) | 0.97 (0.84–1.13) |
| Other circulatory diseases | ||||||
| No. of deaths | 36 | 17 | 15 | 18 | 16 | |
| Model 1 | 1.00 | 0.55 (0.31–0.98) | 0.81 (0.44–1.50) | 0.82 (0.46–1.47) | 1.18 (0.64–2.19) | 0.98 (0.80–1.20) |
| Model 2 | 1.00 | 0.59 (0.33–1.05) | 0.90 (0.49–1.67) | 0.89 (0.49–1.60) | 1.29 (0.68–2.46) | 1.01 (0.82–1.25) |
| Causes other than cancer or CVD | ||||||
| No. of deaths | 151 | 102 | 72 | 74 | 51 | |
| Model 1 | 1.00 | 0.74 (0.58–0.96) | 0.83 (0.62–1.10) | 0.69 (0.52–0.91) | 0.70 (0.50–0.97) | 0.85(0.77–0.94) |
| Model 2 | 1.00 | 0.80 (0.62–1.03) | 0.90 (0.68–1.20) | 0.77 (0.57–1.02) | 0.78 (0.55–1.09) | 0.90(0.82–0.99) |
aThe total follow-up time for men in each quintile of height was 50 916, 59 714, 45 847, 64 861 and 56 308 person-years, respectively.
bModel 1: age adjusted only.
cModel 2: multivariate model: adjusted for birth calendar year, age, education, income, occupation, BMI, WHR, cigarette smoking, alcohol consumption, total physical activity (MET hours/week), daily intake of energy, red meat, fruit and vegetables.
Similar to height, trunk length showed a positive association with cancer mortality, an inverse association with CVD mortality and a null association with total mortality in women and men (Table 4). The results for leg length were less consistent (Table 5). A positive association between leg length and cancer mortality was more evident in men (HR for 1-SD increase: 1.10; 95% CI 1.03–1.16) than in women (HR: 1.03; 95% CI 0.98–1.08), whereas an inverse association with CVD was evident in women (HR for 1-SD increase: 0.91; 95% CI 0.86–0.98), but not in men (HR: 0.99; 95% CI 0.92–1.06).
Table 4.
HRs and 95% CIs of total and cause-specific mortality according to quintiles of trunk lengtha, the SWHS and SMHS
| Quintiles of trunk length |
||||||
|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | Per 1-SD increase | |
| Women | ||||||
| All causes | ||||||
| No. of deaths | 1218 | 807 | 753 | 520 | 326 | |
| Model 1b | 1.00 | 0.84 (0.77–0.92) | 0.90 (0.82–0.98) | 0.85 (0.76–0.94) | 0.85 (0.75–0.97) | 0.94 (0.90–0.97) |
| Model 2c | 1.00 | 0.88 (0.81–0.96) | 0.97 (0.88–1.06) | 0.93 (0.84–1.04) | 0.95 (0.84–1.09) | 0.98 (0.95–1.01) |
| Cancer | ||||||
| No. of deaths | 441 | 363 | 391 | 262 | 196 | |
| Model 1 | 1.00 | 0.98 (0.85–1.13) | 1.15 (1.00–1.32) | 1.01 (0.86–1.18) | 1.15 (0.96–1.38) | 1.05 (1.00–1.10) |
| Model 2 | 1.00 | 1.00 (0.87–1.16) | 1.20 (1.04–1.38) | 1.05 (0.90–1.24) | 1.21 (1.01–1.45) | 1.07 (1.01–1.12) |
| CVD | ||||||
| No. of deaths | 398 | 233 | 199 | 131 | 57 | |
| Model 1 | 1.00 | 0.80 (0.68–0.94) | 0.84 (0.71–1.00) | 0.82 (0.67–1.00) | 0.64 (0.48–0.85) | 0.88 (0.83–0.94) |
| Model 2 | 1.00 | 0.84 (0.72–0.99) | 0.91 (0.76–1.08) | 0.90 (0.73–1.11) | 0.71 (0.54–0.95) | 0.93 (0.87–0.99) |
| Causes other than cancer or CVD | ||||||
| No. of deaths | 379 | 211 | 163 | 127 | 73 | |
| Model 1 | 1.00 | 0.71 (0.60–0.84) | 0.63 (0.52–0.76) | 0.68 (0.55–0.84) | 0.64 (0.49–0.84) | 0.83 (0.78–0.89) |
| Model 2 | 1.00 | 0.78 (0.66–0.92) | 0.73 (0.61–0.88) | 0.82 (0.66–1.01) | 0.79 (0.60–1.03) | 0.91 (0.85–0.97) |
| Men | ||||||
| All causes | ||||||
| No. of deaths | 583 | 412 | 371 | 265 | 229 | |
| Model 1b | 1.00 | 0.86 (0.76–0.98) | 0.89 (0.78–1.02) | 0.81 (0.70–0.94) | 0.99 (0.84–1.16) | 0.91 (0.86–0.96) |
| Model 2c | 1.00 | 0.92 (0.81–1.05) | 0.98 (0.86–1.13) | 0.93 (0.80–1.09) | 1.13 (0.96–1.33) | 0.97 (0.92–1.03) |
| Cancer | ||||||
| No. of deaths | 230 | 174 | 163 | 118 | 123 | |
| Model 1 | 1.00 | 0.91 (0.75–1.11) | 0.97 (0.79–1.19) | 0.89 (0.71–1.12) | 1.29 (1.02–1.63) | 1.03 (0.95–1.13) |
| Model 2 | 1.00 | 0.97 (0.79–1.18) | 1.04 (0.85–1.28) | 1.00 (0.79–1.25) | 1.44 (1.14–1.83) | 1.09 (1.00–1.19) |
| CVD | ||||||
| No. of deaths | 202 | 134 | 132 | 78 | 56 | |
| Model 1 | 1.00 | 0.84 (0.67–1.04) | 0.99 (0.79–1.23) | 0.76 (0.58–0.99) | 0.81 (0.59–1.10) | 0.84 (0.76–0.93) |
| Model 2 | 1.00 | 0.88 (0.71–1.10) | 1.07 (0.85–1.34) | 0.84 (0.64–1.11) | 0.87 (0.64–1.20) | 0.89 (0.81–0.98) |
| Causes other than cancer or CVD | ||||||
| No. of deaths | 151 | 104 | 76 | 69 | 50 | |
| Model 1 | 1.00 | 0.82 (0.64–1.05) | 0.67 (0.50–0.88) | 0.75 (0.56–1.01) | 0.74 (0.53–1.03) | 0.80 (0.71–0.89) |
| Model 2 | 1.00 | 0.92 (0.71–1.18) | 0.79 (0.60–1.05) | 0.96 (0.71–1.29) | 0.92 (0.65–1.30) | 0.90 (0.81–1.01) |
aThe range for each quintile: for women: <82.0, 82.0–83.9, 84.0–85.0, 85.1–87.0 and ≥87.1 cm; for men: <88.0, 88.0–89.0, 90.0–91.0, 91.1–93.0 and ≥93.1 cm. The total follow-up time in each quintile of trunk length was 133 962, 150 649, 172 654, 161 064 and 131 065 person-years, respectively, in women and 51 417, 55 091, 62 397, 56 329 and 52 413 person-years, respectively, in men.
bModel 1: age adjusted only.
cModel 2: multivariate model: adjusted for birth calendar year, age, education, income, occupation, BMI, WHR, cigarette smoking, alcohol consumption, total physical activity (MET hours/week), daily intake of energy, red meat, fruit and vegetables and menopause status (for women only).
Table 5.
HRs and 95% CIs of total and cause-specific mortality according to quintiles of leg lengtha, the SWHS and SMHS
| Quintiles of leg length |
||||||
|---|---|---|---|---|---|---|
| Q1 | Increase | Q3 | Q4 | Q5 | Per 1-SD increase | Per 1-SD increase |
| Women | ||||||
| All causes | ||||||
| No. of deaths | 816 | 737 | 790 | 609 | 672 | |
| Model 1b | 1.00 | 0.97 (0.88–1.08) | 1.02 (0.92–1.13) | 0.98 (0.88–1.09) | 0.96 (0.87–1.06) | 0.98 (0.95–1.01) |
| Model 2c | 1.00 | 0.98 (0.89–1.08) | 1.03 (0.94–1.14) | 0.99 (0.89–1.10) | 0.96 (0.86–1.06) | 0.98 (0.94–1.01) |
| Cancer | ||||||
| No. of deaths | 335 | 316 | 370 | 295 | 337 | |
| Model 1 | 1.00 | 1.00 (0.86–1.17) | 1.13 (0.97–1.31) | 1.09 (0.94–1.28) | 1.08 (0.93–1.26) | 1.02 (0.97–1.08) |
| Model 2 | 1.00 | 1.01 (0.87–1.18) | 1.14 (0.98–1.32) | 1.11 (0.95–1.30) | 1.10 (0.94–1.28) | 1.03 (0.98–1.08) |
| CVD | ||||||
| No. of deaths | 274 | 217 | 208 | 154 | 165 | |
| Model 1 | 1.00 | 0.87 (0.73–1.04) | 0.83 (0.70–1.00) | 0.79 (0.65–0.96) | 0.78 (0.64–0.95) | 0.91 (0.85–0.97) |
| Model 2 | 1.00 | 0.88 (0.74–1.05) | 0.86 (0.72–1.03) | 0.82 (0.67–0.99) | 0.80 (0.65–0.97) | 0.91 (0.86–0.98) |
| Causes other than cancer or CVD | ||||||
| No. of deaths | 207 | 204 | 212 | 160 | 170 | |
| Model 1 | 1.00 | 1.07 (0.88–1.30) | 1.10 (0.91–1.33) | 1.04 (0.85–1.28) | 1.00 (0.82–1.23) | 0.98 (0.91–1.04) |
| Model 2 | 1.00 | 1.06 (0.87–1.28) | 1.09 (0.89–1.32) | 1.03 (0.83–1.26) | 0.93 (0.76–1.15) | 0.96 (0.90–1.03) |
| Men | ||||||
| All causes | ||||||
| No. of deaths | 392 | 466 | 420 | 329 | 253 | |
| Model 1b | 1.00 | 1.06 (0.93–1.22) | 1.14 (0.99–1.31) | 1.14 (0.98–1.32) | 1.04 (0.89–1.27) | 1.02 (0.98–1.06) |
| Model 2c | 1.00 | 1.07 (0.94–1.23) | 1.15 (1.00–1.32) | 1.12 (0.97–1.30) | 1.03 (0.88–1.22) | 1.02 (0.98–1.06) |
| Cancer | ||||||
| No. of deaths | 146 | 207 | 176 | 155 | 124 | |
| Model 1 | 1.00 | 1.26 (1.02–1.55) | 1.26 (1.01–1.57) | 1.41 (1.12–1.77) | 1.32 (1.04–1.68) | 1.09 (1.03–1.16) |
| Model 2 | 1.00 | 1.25 (1.01–1.55) | 1.26 (1.01–1.57) | 1.40 (1.11–1.76) | 1.32 (1.04–1.69) | 1.10 (1.03–1.16) |
| CVD | ||||||
| No. of deaths | 139 | 148 | 141 | 97 | 77 | |
| Model 1 | 1.00 | 0.97 (0.77–1.23) | 1.12 (0.89–1.42) | 1.00 (0.77–1.30) | 0.97 (0.73–1.28) | 0.98 (0.92–1.05) |
| Model 2 | 1.00 | 0.99 (0.79–1.26) | 1.17 (0.92–1.48) | 1.00 (0.77–1.31) | 1.00 (0.75–1.33) | 0.99 (0.92–1.06) |
| Causes other than cancer or CVD | ||||||
| No. of deaths | 107 | 111 | 103 | 77 | 52 | |
| Model 1 | 1.00 | 0.92 (0.70–1.19) | 1.00 (0.76–1.31) | 0.94 (0.70–1.27) | 0.74 (0.53–1.04) | 0.95 (0.87–1.03) |
| Model 2 | 1.00 | 0.94 (0.72–1.22) | 1.01 (0.77–1.33) | 0.90 (0.67–1.22) | 0.72 (0.51–1.02) | 0.94 (0.86–1.02) |
aThe range for each quintile: for women: <70.0, 70.0–71.9, 72.0–73.9, 74.0–75.9 and ≥76.0 cm; for men: <76.0, 76.0–78.4, 78.5–80.0, 80.1–82.5 and ≥82.6 cm. The total follow-up time in each quintile of leg length was 129 724, 135 219, 155 764, 141 460 and 189 298 person-years, respectively, in women, and 46 738, 63 667, 60 807, 53 728 and 52 706 person-years, respectively, in men.
bModel 1: age adjusted only.
cModel 2: multivariate model: adjusted for birth calendar year, age, education, income, occupation, BMI, WHR, cigarette smoking, alcohol consumption, total physical activity (MET hours/week), daily intake of energy, red meat, fruit and vegetables and menopause status (for women only).
To address the concern of over-adjustment, we repeated analyses without adjusting for BMI and found similar results. For example, the HRs for cancer mortality per 1-SD increment in height, trunk and leg length were 1.06, 1.06 and 1.03, respectively, in women and 1.13, 1.08 and 1.11, respectively, in men.
We also conducted sensitivity analyses excluding subjects exposed to the Chinese famine during prenatal and childhood periods (born in 1950–1962) and found no material changes in the results. The multivariate-adjusted HRs for cancer mortality per 1-SD increase in height, trunk and leg length were 1.06, 1.06 and 1.03, respectively, in women, and 1.13, 1.09 and 1.09 in men. The corresponding HRs for CVD mortality were 0.89, 0.93 and 0.91 in women, and 0.92, 0.87 and 0.98 in men.
We conducted additional analyses stratified by pre-existing comorbidities and age. Among individuals without pre-existing comorbidities, the fully adjusted HRs (95% CIs) for cancer and CVD mortality per 1-SD increase in height were 1.08 (1.02–1.15) and 0.88 (0.82–0.96), respectively, in women, and 1.15 (1.04–1.27) and 0.92 (0.81–1.04), respectively, in men. Among individuals with pre-existing comorbidities, the corresponding HRs were 1.03 (0.93–1.13) and 0.90 (0.81–1.00) in women, and 1.10 (0.98–1.23) and 0.93 (0.83–1.04) in men. Among women aged ≤55 years, the HRs for cancer and CVD mortality per 1-SD increment in height were 1.16 (1.06–1.28) and 0.92 (0.77–1.10), whereas among women aged >55, the HRs were 1.02 (0.96–1.09) and 0.89 (0.83–0.96). For younger men, the corresponding HRs were 1.25 (1.07–1.46) and 0.88 (0.72–1.09), whereas for older men, the HRs were 1.09 (1.00–1.19) and 0.93 (0.85–1.03).
Discussion
In these two large cohort studies of Chinese women and men, we found that adult height was positively associated with cancer mortality, but was inversely associated with CVD mortality. These findings were generally consistent across sex and independent of SES and lifestyle risk factors.
The findings of our studies of Chinese women and men are in general agreement with those of previous studies conducted in Western populations.8,11–14 The first US National Health and Nutrition Examination Survey (NHANES) and follow-up study8 reported a positive association between adult height and total cancer incidence and found that the association was particularly evident in men, similar to our finding of a stronger association with cancer mortality in men than in women. Likewise, in the Health Professionals Follow-Up Study12 and the Physicians’ Health Study,13 greater adult height was associated with an elevated risk of cancer, particularly cancers of the colon, pancreas, kidney and prostate. A positive association between adult height and post-menopausal breast cancer was found in a pooled analysis of seven major prospective cohort studies conducted in Western countries.22 The association for pre-menopausal breast cancer, however, was less clear in the pooled analysis. No association between height and total cancer mortality was reported in a Finland study, in which the risk ratios of cancer mortality for per 5-cm increase in height were 0.98 in women and 1.02 in men.3 The positive association between height and cancer mortality has also been reported in studies conducted in Asia Pacific region. By including 39 cohort studies from the Asia Pacific Cohort Studies Collaboration, Lee et al.17 found that for each standard-deviation greater height, the risk of cancer mortality was increased by 5% in men and 9% in women. Similar associations were also observed in middle-aged Korean men23 and women.24 Overall, the evidence supports a positive association between height and cancer risk.25
In contrast to the positive association with cancer, height was inversely associated with CVD outcomes in our study and many previous studies.3,15,16 A recent meta-analysis involving 52 studies26 revealed that individuals in the shortest height category had an ∼50% higher risk of CHD morbidity and mortality than those in the highest category. An inverse association of height with stroke was also reported in some studies,17,23,24 but not in others.15,16,27,28
Studies of height in relation to incidence and mortality of cancer and CVD are generally consistent, suggesting that factors associated with adult height may affect both the risk of developing cancer or CVD and the risk of dying from these diseases. Potential mechanisms linking height with health outcomes in adult life remain to be elucidated.25 The divergent findings on the associations with cancer and CVD suggest that adult height may be linked to these diseases through different pathways. Attained adult height is known to be related to early nutrition,2 particularly energy intake,2 and levels of insulin and insulin-like growth factor-1 (IGF-1),12,29,30 which may in turn affect risk of certain types of cancer. Adult height is also related to coronary artery lumen diameter and lung function, which may mediate some of the observed association with CVD.31–34 However, the inverse association between height and CHD was found to persist in studies that accounted for lung function, as measured by forced expiratory volume in 1 s.7 In addition, it is plausible that hereditary factors which determine adult height may play a role in the development of cancer or CVD. A prospective study of twin pairs,35 however, found a significant inverse association between height and CHD mortality within monozygotic discordant twin pairs, suggesting that environmental factors, rather than genetic factors, may account for the association.35 It has been suggested that leg length may be a better measure than overall height of nutritional exposures during childhood and possibly a better predictor of health outcomes in later life. Relatively few prospective studies have evaluated components of adult height in relation to cancer or CVD.7–9 Compared with trunk length, leg length showed a stronger association with cancer mortality in the NHANES Study,8 and the Boyd–Orr study.14 Moreover, the Boyd–Orr cohort study9 found that childhood leg length, but not trunk length, was inversely associated CVD mortality both in men and women. Similar results were observed in the Caerphilly study.7 Leg length also showed a closer inverse association with diabetes and atherosclerosis than trunk length in the Atherosclerosis Risk in Communities Study.10,32 The Whitehall study36 conducted in the UK, however, suggested that although leg length might underlie the inverse association between height and CHD risk factors, trunk length was more closely associated with CHD events. Rogers30 first related childhood IGF-1 to components of height and found no sufficient evidence for the hypothesis that leg length is a better biomarker of childhood IGF-1, whereas trunk length might be even better for females.30 Trunk length appeared to be the component of height that contributed to the associations of height with cancer and CVD mortality in our study.
Overall, our findings in both women and men provide no evidence suggesting that components of height are better predictors of mortality than height. Stroke was the main cause of CVD-related deaths in our study and contributed largely to the inverse association of height with CVD mortality in both women and men. In women, the positive association of height with cancer mortality was more evident for female cancers than for other cancers, whereas in men, the positive association was more evident for smoking-related cancers than for cancers unrelated to smoking. The latter finding appears to contradict previous studies, which showed a positive association for mortality from smoking-unrelated cancers but not smoking-related cancers. Although this discrepancy could not be readily explained, differences in socio-demographic and lifestyle factors among study populations could be a contributor. Adult height was positively related to SES in our and previous studies. Higher SES has been associated with a less healthy lifestyle in China, but a healthy lifestyle in the USA.
Data from historic records and national surveys over the period 1950–2005 in China have demonstrated a strong secular trend toward an increase in stature.37 This trend likely reflects improved socio-economic development. The potential influence of this trend on health, however, remains uncertain, given the complex relationship of adult height with cancer and CVD. Further research is needed to address this issue.
The strengths of our study include the large sample size, the directly measured height and weight, the ability to adjust for SES and other potential confounding variables, and the nearly complete follow-up on outcomes. Our study also has several limitations. We did not collect any information on nutrition, disease history and other environmental factors during early life, and had limited information on birth weight (<10% cohort members provided birth weight information by self-report). We did not have any information on artery lumen diameter or lung function. Therefore, we were unable to evaluate whether these factors may explain the observed associations. In addition, we did not have repeated measures of height over time and were unable to directly address the potential influence of shrinkage due to aging or chronic illness on study results. However, the correlations between age and height and its components in our study population were modest. Furthermore, in stratified analyses, we found similar results among subgroups with and without pre-existing disease. These findings provide no evidence suggesting that failure to account for shrinkage in height might have introduced a serious bias in our main results. However, we could not exclude the possibility that reverse causation bias might account for a much weaker positive association between height and cancer mortality in older women and men compared with their younger counterparts in our study. Finally, findings of our study conducted in urban Shanghai may not be applicable to other populations.
In conclusion, our study found that adult height and, less consistently, its components were positively associated with cancer mortality and inversely associated with CVD mortality in Chinese women and men, suggesting different pathways are involved in linking height to different disease outcomes.
Funding
This work was supported by grants from the US National Institutes of Health (grant numbers R37 CA070867, R01CA82729, R01HL079123 and R01HL095931). N.W., visiting from Fudan University, is supported by D43 TW008313. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Conflict of interest: None declared.
KEY MESSAGES.
Adult height and, less consistently, trunk and leg length are positively associated with cancer mortality and inversely associated with CVD mortality in Chinese women and men.
Different mechanisms may be involved in linking height and its components with cancer and CVD mortality.
References
- 1.van der Pols JC, Bain C, Gunnell D, Smith GD, Frobisher C, Martin RM. Childhood dairy intake and adult cancer risk: 65-y follow-up of the Boyd Orr cohort. Am J Clin Nutr. 2007;86:1722–29. doi: 10.1093/ajcn/86.5.1722. [DOI] [PubMed] [Google Scholar]
- 2.Wadsworth ME, Hardy RJ, Paul AA, Marshall SF, Cole TJ. Leg and trunk length at 43 years in relation to childhood health, diet and family circumstances; evidence from the 1946 national birth cohort. Int J Epidemiol. 2002;31:383–900. [PubMed] [Google Scholar]
- 3.Jousilahti P, Tuomilehto J, Vartiainen E, Eriksson J, Puska P. Relation of adult height to cause-specific and total mortality: a prospective follow-up study of 31,199 middle-aged men and women in Finland. Am J Epidemiol. 2000;151:1112–20. doi: 10.1093/oxfordjournals.aje.a010155. [DOI] [PubMed] [Google Scholar]
- 4.Cook NR, Hebert PR, Satterfield S, Taylor JO, Buring JE, Hennekens CH. Height, lung function, and mortality from cardiovascular disease among the elderly. Am J Epidemiol. 1994;139:1066–76. doi: 10.1093/oxfordjournals.aje.a116950. [DOI] [PubMed] [Google Scholar]
- 5.Smith GD, Shipley M, Leon DA. Height and mortality from cancer among men: prospective observational study. BMJ. 1998;317:1351–52. doi: 10.1136/bmj.317.7169.1351. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Kannam JP, Levy D, Larson M, Wilson PW. Short stature and risk for mortality and cardiovascular disease events. The Framingham Heart Study. Circulation. 1994;90: 2241–47. doi: 10.1161/01.cir.90.5.2241. [DOI] [PubMed] [Google Scholar]
- 7.Davey Smith G, Greenwood R, Gunnell D, Sweetnam P, Yarnell J, Elwood P. Leg length, insulin resistance, and coronary heart disease risk: the Caerphilly Study. J Epidemiol Community Health. 2001;55:867–72. doi: 10.1136/jech.55.12.867. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Albanes D, Jones DY, Schatzkin A, Micozzi MS, Taylor PR. Adult stature and risk of cancer. Cancer Res. 1988;48:1658–62. [PubMed] [Google Scholar]
- 9.Gunnell DJ, Davey Smith G, Frankel S, et al. Childhood leg length and adult mortality: follow up of the Carnegie (Boyd Orr) Survey of Diet and Health in Pre-war Britain. J Epidemiol Community Health. 1998;52:142–52. doi: 10.1136/jech.52.3.142. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Weitzman S, Wang CH, Pankow JS, Schmidt MI, Brancati FL. Are measures of height and leg length related to incident diabetes mellitus? The ARIC (Atherosclerosis Risk in Communities) study. Acta Diabetol. 2010;47:237–42. doi: 10.1007/s00592-009-0145-0. [DOI] [PubMed] [Google Scholar]
- 11.Batty GD, Shipley MJ, Langenberg C, Marmot MG, Davey Smith G. Adult height in relation to mortality from 14 cancer sites in men in London (UK): evidence from the original Whitehall study. Ann Oncol. 2006;17:157–66. doi: 10.1093/annonc/mdj018. [DOI] [PubMed] [Google Scholar]
- 12.Giovannucci E, Rimm EB, Liu Y, Willett WC. Height, predictors of C-peptide and cancer risk in men. Int J Epidemiol. 2004;33:217–25. doi: 10.1093/ije/dyh020. [DOI] [PubMed] [Google Scholar]
- 13.Hebert PR, Ajani U, Cook NR, Lee IM, Chan KS, Hennekens CH. Adult height and incidence of cancer in male physicians (United States) Cancer Causes Control. 1997;8:591–97. doi: 10.1023/a:1018442329319. [DOI] [PubMed] [Google Scholar]
- 14.Gunnell DJ, Davey Smith G, Holly JM, Frankel S. Leg length and risk of cancer in the Boyd Orr cohort. BMJ. 1998;317:1350–51. doi: 10.1136/bmj.317.7169.1350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Hebert PR, Rich-Edwards JW, Manson JE, et al. Height and incidence of cardiovascular disease in male physicians. Circulation. 1993;88:1437–43. doi: 10.1161/01.cir.88.4.1437. [DOI] [PubMed] [Google Scholar]
- 16.Rich-Edwards JW, Manson JE, Stampfer MJ, et al. Height and the risk of cardiovascular disease in women. Am J Epidemiol. 1995;142:909–17. doi: 10.1093/oxfordjournals.aje.a117738. [DOI] [PubMed] [Google Scholar]
- 17.Lee CM, Barzi F, Woodward M, et al. Adult height and the risks of cardiovascular disease and major causes of death in the Asia-Pacific region: 21,000 deaths in 510,000 men and women. Int J Epidemiol. 2009;38:1060–71. doi: 10.1093/ije/dyp150. [DOI] [PubMed] [Google Scholar]
- 18.Batty GD, Barzi F, Woodward M, et al. Adult height and cancer mortality in Asia: the Asia Pacific Cohort Studies Collaboration. Ann Oncol. 2010;21:646–54. doi: 10.1093/annonc/mdp363. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Goldbourt U, Tanne D. Body height is associated with decreased long-term stroke but not coronary heart disease mortality? Stroke. 2002;33:743–8. doi: 10.1161/hs0302.103814. [DOI] [PubMed] [Google Scholar]
- 20.Zheng W, Chow WH, Yang G, et al. The Shanghai Women's Health Study: rationale, study design, and baseline characteristics. Am J Epidemiol. 2005;162:1123–31. doi: 10.1093/aje/kwi322. [DOI] [PubMed] [Google Scholar]
- 21.Cai H, Zheng W, Xiang YB, et al. Dietary patterns and their correlates among middle-aged and elderly Chinese men: a report from the Shanghai Men's Health Study. Br J Nutr. 2007;98:1006–13. doi: 10.1017/S0007114507750900. [DOI] [PubMed] [Google Scholar]
- 22.van den Brandt PA, Spiegelman D, Yaun SS, et al. Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol. 2000;152:514–27. doi: 10.1093/aje/152.6.514. [DOI] [PubMed] [Google Scholar]
- 23.Song YM, Davey Smith G, Sung J. Adult height and cause-specific mortality: a large prospective study of South Korean men. Am J Epidemiol. 2003;158:479–85. doi: 10.1093/aje/kwg173. [DOI] [PubMed] [Google Scholar]
- 24.Song YM, Sung J. Adult height and the risk of mortality in South Korean women. Am J Epidemiol. 2008;168:497–505. doi: 10.1093/aje/kwn187. [DOI] [PubMed] [Google Scholar]
- 25.Gunnell D, Okasha M, Davey Smith G, Oliver SE, Sandhu J, Holly JM. Height, leg length, and cancer risk: a systematic review. Epidemiol Rev. 2001;23:313–42. doi: 10.1093/oxfordjournals.epirev.a000809. [DOI] [PubMed] [Google Scholar]
- 26.Paajanen TA, Oksala NK, Kuukasjarvi P, Karhunen PJ. Short stature is associated with coronary heart disease: a systematic review of the literature and a meta-analysis. Eur Heart J. 2010;31:1802–9. doi: 10.1093/eurheartj/ehq155. [DOI] [PubMed] [Google Scholar]
- 27.Whitley E, Martin RM, Davey Smith G, Holly JM, Gunnell D. The association of childhood height, leg length and other measures of skeletal growth with adult cardiovascular disease: the Boyd-Orr cohort. J Epidemiol Community Health. 2010 doi: 10.1136/jech.2009.104216. doi:10.1136/jech.2009.104216. [DOI] [PubMed] [Google Scholar]
- 28.Hozawa A, Murakami Y, Okamura T, et al. Relation of adult height with stroke mortality in Japan: NIPPON DATA80. Stroke. 2007;38:22–6. doi: 10.1161/01.STR.0000251806.01676.60. [DOI] [PubMed] [Google Scholar]
- 29.Ji CY, Chen TJ. Secular changes in stature and body mass index for Chinese youth in sixteen major cities, 1950s–2005. Am J Hum Biol. 2008;20:530–37. doi: 10.1002/ajhb.20770. [DOI] [PubMed] [Google Scholar]
- 30.Holly J. Insulin-like growth factor-I and new opportunities for cancer prevention. Lancet. 1998;351:1373–75. doi: 10.1016/S0140-6736(05)79438-1. [DOI] [PubMed] [Google Scholar]
- 31.Rogers I, Metcalfe C, Gunnell D, Emmett P, Dunger D, Holly J. Insulin-like growth factor-I and growth in height, leg length, and trunk length between ages 5 and 10 years. J Clin Endocrinol Metab. 2006;91:2514–19. doi: 10.1210/jc.2006-0388. [DOI] [PubMed] [Google Scholar]
- 32.Palmer JR, Rosenberg L, Shapiro S. Stature and the risk of myocardial infarction in women. Am J Epidemiol. 1990;132:27–32. doi: 10.1093/oxfordjournals.aje.a115639. [DOI] [PubMed] [Google Scholar]
- 33.Tilling K, Lawlor DA, Davey Smith G, Chambless L, Szklo M. The relation between components of adult height and intimal-medial thickness in middle age: the Atherosclerosis Risk in Communities Study. Am J Epidemiol. 2006;164:136–42. doi: 10.1093/aje/kwj184. [DOI] [PubMed] [Google Scholar]
- 34.Walker M, Shaper AG, Phillips AN, Cook DG. Short stature, lung function and risk of a heart attack. Int J Epidemiol. 1989;18:602–6. doi: 10.1093/ije/18.3.602. [DOI] [PubMed] [Google Scholar]
- 35.Gunnell D, Whitley E, Upton MN, McConnachie A, Davey Smith G, Watt GC. Associations of height, leg length, and lung function with cardiovascular risk factors in the Midspan Family Study. J Epidemiol Community Health. 2003;57:141–46. doi: 10.1136/jech.57.2.141. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Silventoinen K, Zdravkovic S, Skytthe A, et al. Association between height and coronary heart disease mortality: a prospective study of 35,000 twin pairs. Am J Epidemiol. 2006;163:615–21. doi: 10.1093/aje/kwj081. [DOI] [PubMed] [Google Scholar]
- 37.Ferrie JE, Langenberg C, Shipley MJ, Marmot MG. Birth weight, components of height and coronary heart disease: evidence from the Whitehall II study. Int J Epidemiol. 2006;35:1532–42. doi: 10.1093/ije/dyl184. [DOI] [PubMed] [Google Scholar]