Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A prospective study of the relationship between body mass index and cataract extraction among US women and men


BACKGROUND: Obesity may influence several physiologic processes involved in cataract formation such as oxidative stress, glycosylation and osmotic stress.

OBJECTIVE: To examine the association between increased body mass index (BMI) and the incidence of cataract extraction.

DESIGN AND SETTING: The Nurses' Health Study and the Health Professionals Follow-up Study, both prospective cohort studies of US women and men.

SUBJECTS: A total of 87 682 women and 45 549 men aged 45 y and older who did not have diagnosed cataract or cancer at baseline (1980 for women, 1986 for men).

MEASUREMENTS: Cataract extractions occurring between baseline and 1996, confirmed by medical records.

RESULTS: During 16 y of follow-up in the women, and 10 y in the men, (1 097 997 person-y), 4430 incident cases were documented. Compared to participants with BMI less than 23 kg/m2, those with BMI greater than or equal to 30 kg/m2 had 36% higher risk of any type of cataract (pooled multivariate relative risk (RR), 1.36; 95% CI, 1.23–1.49) after adjusting for smoking, age and lutein/zeaxanthin intake. The association was strongest for posterior subcapsular (PSC) cataract (pooled multivariate RR, 1.99; 95% CI, 1.55–2.55). With adjustment for diabetes, the RR of obesity associated with posterior subcapsular cataract was 1.68 (95% CI, 1.30–2.17). Obesity was not significantly associated with nuclear cataract.

CONCLUSION: Obesity increases the risk of developing cataract overall, and of PSC cataract in particular; the etiology of PSC cataract may be mediated at least in part by glucose intolerance and insulin resistance, even in the absence of clinical diabetes.


Cataract is the leading cause of blindness worldwide and, while loss of vision due to cataract is correctable by surgery, this procedure is expensive and not readily available in less developed countries. Even where surgical procedures are accessible, the risk of complications such as posterior capsule opacification may range from 10 to 50%.1,2,3,4,5 With between 1.6 and 2 million procedures each year, cataract extraction is the number one therapeutic surgical procedure performed in the US. It is also the costliest outpatient procedure; the average charge for a cataract extraction with intraocular lens insertion is $3120 (in 1995 dollars).6,7,8 The prevention of cataract or delaying progression to visual disability would save money and resources in areas where surgical procedures are available, and improve the health and well-being of the elderly regardless of access to surgical procedures.

There are three principal mechanisms by which lens cells are damaged, leading to cataract: oxidative stress, osmotic gradients and glycosylation. The established risk factors and protective factors for age-related cataract such as diabetes, steroid use and heavy smoking are each associated with one or more of these mechanisms.9,10,11 Obesity may influence these physiological processes as well.12,13,14 Animal models support a role for calorie restriction in preventing cataract.15,16,17 Some18,19,20,21,22 but not all23,24,25 observational studies have demonstrated an association between anthropometric measures and increased risk for nuclear, posterior subcapsular (PSC) and mixed opacities. However most of these studies were cross-sectional in design, and none evaluated whether risk differed by history of diabetes. Therefore, we examined prospectively the association between body mass index and the incidence of cataract extraction among participants enrolled in the Nurses' Health Study and the Health Professionals Follow-up Study.


The Nurses' Health Study began in 1976 when 121 700 female registered nurses 30–55 y of age and residing in 11 states returned a mailed questionnaire on medical history, use of oral contraceptives, and risk factors for cancer and cardiovascular disease.26 Information on lifestyle factors and disease has been collected through biennial mailed questionnaires since 1976. For the women, we selected 1980 as the baseline because this was the first year in which diet was assessed among 92 468 (76%) of the Nurses' Health Study participants, and because extractions were confirmed only in this population. The Health Professionals Follow-up Study is a prospective investigation of dietary etiologies of chronic disease among 51 529 US male health professionals residing in 50 US states and aged 40–75 in 1986.27 The study population consists of 29 683 dentists, 3743 optometrists, 2218 osteopathic physicians, 4185 pharmacists, 1600 podiatrists, and 10 098 veterinarians who returned a mailed questionnaire in 1986. A total of 49 933 completed food frequency questionnaires in 1986. As with the women, the men are followed using mailed questionnaires every 2 y.

Body mass index

Height and weight were assessed at baseline and weight was updated on each subsequent questionnaire in each cohort. The baseline questionnaires also asked about weight at age 18 for the women, or age 21 for the men. We calculated body mass index (BMI) as the weight in kilograms divided by the height in meters squared. When weight was missing for an interval, we assumed that weight did not change from the previous cycle. BMI was classified into five categories based on NHLBI guidelines,28 with normal weight split into two categories in order to facilitate comparison with other studies; the categories used were: normal (<23 kg/m2 and 23–24.9 kg/m2), overweight (25–27.9 kg/m2 and 28–29.9 kg/m2) and obese (≥30 kg/m2). We calculated weight change as the difference between weight at baseline and reported weight at age 18 or 21. Self-reported current weight has been shown to be highly correlated with measured weight in subgroups of each cohort with correlations ranging from 0.96 to 0.97.29,30 The validity of recalled weight at age 18 was evaluated in our cohort of younger women (the Nurses' Health Study II). The correlation between recalled weight and that documented in college or nursing school records was 0.84. This correlation was identical in women less than and more than 35 y of age.31 Self-reported height is generally accepted to be highly valid.31,32,33

Other exposure variables

From the questionnaires, we obtained information on age, diagnosis of diabetes, smoking status, physical activity, number of visits to a doctor, and newly diagnosed medical conditions. Pack years of smoking were calculated by multiplying the number of packs (20 cigarettes) smoked per day by the number of years over which time that amount was smoked. In the Nurses' Health Study, dietary intake, including supplement use, was assessed by a semiquantitative food frequency questionnaire (FFQ) in 1980, 1984, 1986, 1990 and 1994; in the Health Professionals Follow-up Study, a similar FFQ was completed in 1986 at baseline and again in 1990 and 1994. We included lutein/zeaxanthin intake as a covariate because this nutrient is most strongly related to cataract extraction in these cohorts.34,35 In the Nurses' Health Study we averaged nutrient intakes from the 1980 and 1984 questionnaires to dampen the within-person variation in diet, while still capturing past dietary patterns; in the Health Professionals Follow-up Study we used baseline values.

Population for analysis

We restricted the baseline population to women and men who were older than age 45 at baseline. Those too young at baseline entered the analysis in the 2 y cycle after they reached age 45. We included only those who completed a baseline FFQ, and who had plausible energy intakes (between 500 and 3500 kcal/day for women, or between 800 and 4200 kcal/day for men) or who had fewer than 70 blank items on the FFQ (n=92 468 women, 49 933 men).

The analysis included 16 y of follow-up for the women, and 10 y of follow-up for the men. We excluded from the analysis all participants diagnosed as having cataract (n=2401) or cancer (except non-melanoma skin cancer) at baseline (n=5596). We also excluded those for whom we had no information on height or weight at baseline (n=1173). This left a total of 133 231 participants available for follow-up. In our cohort for analysis, follow-up through 1996 was 90% in the women and 91% in the men.


Participants were asked if they had a cataract extraction starting in 1984 for the Nurses' Health Study and in 1988 for the Health Professionals Follow-up Study and, if so, for permission to review medical records. We then contacted the ophthalmologist who performed the surgery and, when available, the patient's optometrist or other health care provider who had ophthalmologic records to confirm the dates of initial diagnosis and extraction and to determine any known cause of the cataract. From the records, we obtained the participant's best corrected visual acuity in both eyes prior to surgery and the location of the lens opacity in each eye. All of the ophthalmologists who responded to the questionnaire confirmed the extraction and 86% of the confirmed dates of extraction were within 6 months of the participants' reports, therefore we also included 538 cases confirmed on a supplementary questionnaire by the participant but for whom we had no information from their eye doctor, for a total of 5425 confirmed cases.

We excluded cataracts considered by the physician to be either congenital or secondary to chronic steroid use, chronic intraocular inflammation, ocular trauma, previous intraocular surgery, or glaucoma (n=505). These cases accrued person-time up until the date the cataract was initially diagnosed, determined as the earlier of dates indicated by the participant or ophthalmologist. We also excluded cases who developed cancer before cataract diagnosis; these individuals accrued person-time up until the date of cancer diagnosis, and were not counted as cases (n=100). Finally, we excluded those for whom no date of diagnosis was available (n=303), and those whose cataracts were diagnosed prior to age 45 (n=87). In total, 3241 cataract cases in the women and 1189 in the men were included in the analysis.

Opacities in different areas of the lens (nuclear, posterior subcapsular (PSC), or cortical) may have differing etiologies.36,37,25 The principal analysis is based on three cataract subtypes: (1) any cataract without regard to lens location (n=4430); (2) only nuclear cataract in either eye if unilateral, or both eyes if bilateral as determined by the participant's eye doctor (n=1261); similarly (3) posterior subcapsular cataract in either eye if unilateral, or both eyes if bilateral (n=573). We used nuclear or PSC ‘only’ definitions (rather than ‘any’ or ‘primarily’ nuclear/PSC) to minimize the possibility of misclassification of cataract subtype by the reporting eye doctors. We did not separately examine the cortical cataract subtype because, while cortical cataracts are common, they do not typically result in disabling impairment of vision and thus were underrepresented among our surgical extraction cases (n=138).

Data analysis

Each participant's follow-up time began with the date of return of the 1980 questionnaire for the women and the 1986 questionnaire for the men, or the time when they reached age 45, whichever was later. To avoid bias due to ophthalmologists who suggest interventions that could affect BMI after diagnosis of cataract, we censored cases at the date of diagnosis rather than the date of surgery. Because participants who do not have eye exams may be less likely to have cataracts diagnosed, men and women did not contribute to the analysis during any 2 y interval if they did not report having an eye exam in that period, as reported on the questionnaires from 1990 forward (6299 participants never had an eye exam during these periods). Follow-up continued until the report of cataract diagnosis, death, cancer, loss to follow-up, or end of follow-up (June 1996 for the women, January 1996 for the men), whichever came first, for a total of 1 097 997 person-y.

In the primary analysis, we used incidence rates with person-y of follow-up as the denominator. For each participant, person-months were allocated according to the baseline exposure and then updated according to information on subsequent 2 y follow-up questionnaires. Relative risks were calculated as the rate of occurrence of cataract extraction in each category of body mass index, divided by the corresponding rate in the reference category. All RRs were age-adjusted by 5 y intervals, and 95% confidence intervals were calculated. To control for age and other risk factors simultaneously, we used pooled binary and polytomous logistic regression with 2 y increments, asymptotically equivalent to Cox regression with time dependent covariates.38 We used the binary model for the ‘any cataract’ analysis and the polytomous logistic regression model to evaluate differences in risk factors by cataract type. We computed the Lagrange multiplier test statistic as a measure of the differences between type-specific coefficients.39,40

We conducted separate analyses for each cohort and pooled the two study groups to achieve maximum statistical power. Tests for heterogeneity between the two study groups were conducted, and meta-analytic methods using a random effects model were employed to pool the relative risks from the two cohorts.41


At baseline, 40% of the women and 17% of the men had BMI less than 23 kg/m2; 12% of the women and 8% of the men had BMI 30 kg/m2 or greater (meeting the international definition of obesity). Compared to those in the lowest BMI category, more of the women in the high BMI group had never smoked, whereas the reverse was observed in the men. Other baseline characteristics are shown in Table 1.

Table 1 Age-adjusteda risk factor prevalences at baseline (1980 for Nurses' Health Study, 1986 for Health Professionals Follow-up Study)

Women and men with higher body mass index had a significantly increased risk of cataract extraction, and the risk increased linearly with increasing BMI (Table 2). Because diabetes may be an intermediate in the causal pathway between obesity and cataract extraction, in our primary analyses we did not adjust for diabetes. Compared to participants with BMI less than 23 kg/m2, those with BMI greater than or equal to 30 kg/m2 had a 36% higher risk of developing any type of cataract, after adjusting for smoking, age, and dietary intake of lutein/zeaxanthin, (pooled multivariate RR, 1.36; 95% CI, 1.23–1.49, P-value for heterogeneity=0.64, P-trend<0.001). The association was strongest for PSC cataract (pooled multivariate RR, 1.99; 95% CI, 1.55–2.55, P-value for heterogeneity=0.62, P-trend < 0.001). Obesity was not significantly associated with nuclear cataract (pooled multivariate RR, 1.05; 95% CI, 0.88–1.26, P-trend=0.780).

Table 2 Relative risk of cataract extraction by body mass index

After further adjusting for diabetes, the relative risks decreased. Compared with participants with BMI less than 23 kg/m2, those with BMI greater than or equal to 30 kg/m2 had a 23% higher risk of developing any type of cataract (pooled multivariate RR, 1.23; 95% CI, 1.12–1.36, P-trend <0.001), and a 68% higher risk of PSC cataract, (pooled multivariate RR, 1.68; 95% CI, 1.30–2.17, P-trend <0.001). Further adjusting for duration of diabetes did not change the results. The association between obesity and nuclear cataract remained null when we adjusted for diabetes. For both women and men, these relative risks were similar across strata of smoking (P-values, likelihood ratio tests for any cataract=0.24 for women and 0.62 for men, nuclear=0.35 for women and 0.18 for men, and PSC=0.33 for women and 0.37 for men).

When we excluded those participants with diabetes two years before they reported a diagnosis, the relationship between BMI and cataract was only slightly attenuated. For PSC cataract, the pooled multivariate RR was 1.61 (95% CI, 1.25–2.09, P-value for heterogeneity=0.82). Excluding diabetics from the analysis entirely did not materially change the results. Among non-diabetics with BMI greater than or equal to 30 kg/m2, the pooled multivariate relative risk of PSC cataract was 1.80 (95% CI, 1.36–2.37, P-value for heterogeneity=0.67) compared with those with BMI less than 23 kg/m2. Similarly, the relative risks for nuclear cataract were essentially unchanged. When we performed the analysis in only those women with diabetes, the relative risk of PSC cataract was similar to that observed in the analysis adjusting for diabetes, but the confidence intervals were wide (multivariate RR, 1.59; 95% CI, 0.65–3.85, P-trend=0.25).

Change in weight from early adulthood (age 18 in the women, and age 21 in the men) to baseline weight was more modestly associated with risk of cataract extraction than current BMI. In pooled multivariate analysis adjusting for BMI at age 18 or 21, age, smoking and lutein/zeaxanthin intake, the relative risk of any type of cataract extraction associated with gaining 20.5 kg or more was 1.29 (95% CI, 1.15–1.44, P-value for heterogeneity between the cohorts=0.72, P-trend<0.001) compared with those with stable weight. For PSC cataract, the comparable pooled multivariate RR was 1.57 (95% CI, 1.18–2.09, P-value for heterogeneity between the cohorts=0.32, P-trend<0.001). When we stratified by BMI at age 18 or 21, those who were in the lowest quintile of BMI at age 18 or 21 and the highest quintile of current weight had the highest risk of PSC cataract extraction (RR, 4.17; 95% CI, 2.21–7.89, P-value for heterogeneity=0.40). There was no significant association between change in weight and risk of nuclear cataract. Results were attenuated when we further adjusted for diabetes. We observed similar associations when we updated change in weight at each questionnaire. When we examined recent (in the last 4 y) weight change, there was a significant association in models before adjusting for diabetes or current weight, but the association became nonsignificant upon adding current weight and/or diabetes to the models.

In polytomous logistic regression with mutually exclusive categories, the multivariate relative risk of a 5 kg/m2 increase in BMI was higher for PSC cataract (women: RR, 1.27 (95% CI, 1.18–1.38); men: RR, 1.29 (95% CI, 1.06–1.57)) than for either nuclear (women: RR, 1.03 (95% CI, 0.97–1.10), men: RR, 1.04 (95% CI, 0.87–1.24)) or mixed cataract (women: RR, 1.15 (95% CI, 1.10–1.21); men: RR, 1.04 (95% CI, 0.93–1.15) and the type difference was significant in the women (P-value, Lagrange test <0.001) but not the men (P-value, Lagrange test=0.151). These relative risks were slightly attenuated when we further adjusted for diabetes (Table 3). As expected, differences were seen by cataract subtype for risk of diabetes, with female diabetics having 2.88 (95% CI, 2.17–3.81) times the risk of PSC cataract and males having 2.52 (95% CI, 1.52–4.18) times the risk compared to non-diabetics in multivariate analysis including BMI in the model. The association between diabetes and nuclear cataract was weaker and nonsignificant.

Table 3 Multivariatea odds ratios and test for type-specific differences for body mass index and diabetes in polytomous logistic regression


We observed a significant positive association between BMI and risk of cataract extraction in two large cohorts of women and men; the associations were strongest for PSC cataract (13% of cases). Our data do not support a relationship between obesity and nuclear cataract.

Higher BMI is a known risk factor for diabetes, and diabetics are at higher risk of PSC cataract,25,42,43 raising the question of whether BMI influences risk of cataract through the osmotic and glycosylation pathways associated with diabetes and/or via a mechanism unique to higher BMI. If those with high BMI are merely undiagnosed diabetics, then the independent risk we observed could be due to BMI acting as a proxy for diabetes. Although some number of participants with high BMI are actually undiagnosed diabetics, this number is probably small; in a pilot study among 189 women in the Nurses' Health Study who had not reported a diagnosis of diabetes, only 3.2% of the participants had hemoglobin A1c levels greater than the threshold level of 6.5% (generally considered to indicate diabetes). Neither excluding diabetics from our analysis entirely nor censoring diabetics 2 y prior to their diagnosis changed our results. This suggests an independent role for higher BMI in the development of cataract, over and above the risk associated with diabetes.

Other biological mechanisms besides diabetes might explain the relationship between obesity and cataract. First, reduced glycemic control in obese patients who do not have clinically significant diabetes may contribute to the pathophysiology of cataract amongst the obese. Second, an inflammatory mechanism may be involved: elevated levels of C-reactive protein and plasma fibrinogen have been associated with obesity,44,45,46,47 and recently these markers of inflammation, such as fibrinogen, have been associated with cataract.23,48

Our results are consistent with those seen in two other studies. In the Physicians Health Study, compared to men with BMI less than 22 kg/m2, men with BMI 27.8 kg/m2 or greater had 1.45 times the risk of cataract surgery (95% CI, 1.13–1.84). The corresponding risks for any nuclear and any PSC opacities in the Physicians Health Study were 1.26 (95% CI, 1.03–1.55) and 1.38 (95% CI, 1.02–1.86), respectively.22 In the Framingham Eye Study risk of any type of lens opacity, including aphakia was 1.76 times higher for participants whose BMI was 27.8 kg/m2 or greater (vs <22 kg/m2) approximately 15 y before the eye examination, with relative risks of 0.98 (95% CI, 0.52–1.85) for nuclear opacities and 1.94 (95% CI, 0.69–5.44) for PSC opacities (not including aphakics).18

The apparent inconsistency in results for nuclear cataract could be due to differences in how the endpoints were defined and measured in different studies. We used cataract extraction as our endpoint, which ensures little misclassification of non-cases as cases. To the extent that certain cataract types progress at different rates to the point where they are disabling and thus require extraction, this method of case definition enabled us to closely examine the association of BMI with PSC cataracts, a subtype which is underrepresented in studies of lens opacities that exclude aphakics. Defining the endpoint as cataract extraction rather than cataract diagnosis also decreases the chance for variation in the threshold for diagnosis of disease.

Our results could be biased if those who had higher BMI were more likely to have their cataracts extracted at an earlier stage than those with lower BMI. Diabetics, who tend to have higher BMI, are also more likely to have regular eye exams since they are at risk for retinopathy. These patients might have cataracts, particularly the PSC type, diagnosed and extracted at an earlier stage than a non-diabetic would, since PSC cataract is more likely to impair visualization of the retina than cataracts in other locations of the lens. However, when we excluded all diagnosed diabetics from our analysis, our results remained essentially unchanged, indicating that this diagnostic bias is not a large problem in these cohorts. To examine this issue further, we assessed the Spearman correlation between body mass index and visual acuity before surgery in the eye being operated on as a measure of disease severity. These correlations were quite small (range from 0.02 to 0.04).

Data on exposure were collected before diagnosis, thus any misclassification is unrelated to risk of cataract and would tend to bias our results toward the null. We used date of diagnosis as our endpoint to minimize bias that could result if participants lost weight as a result of being diagnosed with cataract. The high follow-up rates in these cohorts minimize loss to follow-up as a source of bias. We controlled for most known risk factors for cataract extraction in our analyses, however the effect of confounding by unmeasured risk factors could conceivably have distorted the associations.

Our finding that obesity increases the risk of developing any type of cataract, and of PSC cataract in particular, supports the hypothesis that the etiology of PSC cataract is mediated at least in part by glucose intolerance and insulin resistance,19 even in the absence of clinical diabetes. Obesity is related to numerous chronic diseases, and the causal relationship between obesity and cataract extraction, both independently and as mediated by diabetes, adds further indication of the burden of obesity on our society.


  1. 1

    Clark DS, Munsell MF, Emery JM . Mathematical model to predict the need for neodymium: YAG capsulotomy based on posterior capsule opacification rate J Cataract Refract Surg 1998 24: 1621–1625.

    CAS  Article  Google Scholar 

  2. 2

    Hayashi H, Hayashi K, Nakao F, Hayashi F . Quantitative comparison of posterior capsule opacification after polymethylmethacrylate, silicone, and soft acrylic intraocular lens implantation Arch Ophthal 1998 116: 1579–1582.

    CAS  Article  Google Scholar 

  3. 3

    Hsieh WC . Review of the medical management of postoperative cataract complications J Am Optom Assoc 1998 69: 465–472.

    CAS  Google Scholar 

  4. 4

    Sundelin K, Sjostrand J . Posterior capsule opacification 5 years after extracapsular cataract extraction J Cataract Refract Surg 1999 25: 246–250.

    CAS  Article  Google Scholar 

  5. 5

    Ursell PG, Spalton DJ, Pande MV et al. Relationship between intraocular lens biomaterials and posterior capsule opacification J Cataract Refract Surg 1998 24: 352–360.

    CAS  Article  Google Scholar 

  6. 6

    Health Care Finance Administration Press Office. Press Release 1995

  7. 7

    Dorgan C . Statistical Record of Health and Medicine Gale Research Inc.: Detroit, MI 1995

    Google Scholar 

  8. 8

    Four costliest outpatient procedures Hospitals Health Netw 1997 30–31.

  9. 9

    Hankinson SE . The epidemiology of age-related cataract. In: Albert D, Jakobiec F (eds) Principles and practice of ophthalmology WB Saunders: Philadelphia, PA 1994

    Google Scholar 

  10. 10

    West SK, Valmadrid CT . Epidemiology of risk factors for age-related cataract Surv Ophthal 1995 39: 323–334.

    CAS  Article  Google Scholar 

  11. 11

    Mohan M, Sperduto RD, Angra SK et al. India–US case–control study of age-related cataracts Arch Ophthal 1989 107: 670–676.

    CAS  Article  Google Scholar 

  12. 12

    Serrano Rios M . Relationship between obesity and the increased risk of major complications in non-insulin-dependent diabetes mellitus Eur J Clin Invest 1998 28S: 14–17.

    Article  Google Scholar 

  13. 13

    Bakker SJ, Ijzerman RG, Teerlink T, Westerhoff HV, Gans RO, Heine RJ . Cytosolic triglycerides and oxidative stress in central obesity: the missing link between excessive atherosclerosis, endothelial dysfunction, and beta-cell failure? Atherosclerosis 2000 148: 17–21.

    CAS  Article  Google Scholar 

  14. 14

    Vincent HK, Powers SK, Stewart DJ, Shanely RA, Demirel H, Naito H . Obesity is associated with increased myocardial oxidative stress Int J Obes Relat Metab Disord 1999 23: 67–74.

    CAS  Article  Google Scholar 

  15. 15

    Li Y, Yan Q, Wolf NS . Long-term caloric restriction delays age-related decline in proliferation capacity of murine lens epithelial cells in vitro and in vivo Invest Ophthal Visual Sci 1997 38: 100–107.

    CAS  Google Scholar 

  16. 16

    Li Y, Yan Q, Pendergrass WR, Wolf NS . Response of lens epithelial cells to hydrogen peroxide stress and the protective effect of caloric restriction Exp Cell Res 1998 239: 254–263.

    CAS  Article  Google Scholar 

  17. 17

    Taylor A, Zuliani AM, Hopkins RE et al. Moderate caloric restriction delays cataract formation in the Emory mouse FASEB J 1989 3: 1741–1746.

    CAS  Article  Google Scholar 

  18. 18

    Hiller R, Podgor MJ, Sperduto RD et al. A longitudinal study of body mass index and lens opacities—the Framingham Studies Ophthalmology 1998 105: 1244–1250.

    CAS  Article  Google Scholar 

  19. 19

    Karasik A, Modan M, Halkin H, Treister G, Fuchs Z, Lusky A . Senile cataract and glucose intolerance: the Israel Study of Glucose Intolerance Obesity and Hypertension (The Israel GOH Study) Diabetes Care 1984 7: 52–56.

    CAS  Article  Google Scholar 

  20. 20

    Klein BE, Klein R, Moss SE . Incidence of self reported glaucoma in people with diabetes mellitus Br J Ophthal 1997 81: 743–747.

    CAS  Article  Google Scholar 

  21. 21

    Leske M, Wu S, Hennis A, Connell A, Hyman L, Schachat A . Diabetes, hypertension, and central obesity as cataract risk factors in a black population—the Barbados Eye Study Ophthalmology 1999 106: 35–41.

    CAS  Article  Google Scholar 

  22. 22

    Schaumberg DA, Glynn RJ, Christen WG, Hankinson SE, Hennekens CH . Relationships of body fat distribution and height with cataract in men Am J Clin Nutr 2000 72: 1495–1502.

    CAS  Article  Google Scholar 

  23. 23

    Goodrich ME, Cumming RG, Mitchell P, Koutts J, Burnett L . Plasma fibrinogen and other cardiovascular disease risk factors and cataract Ophthal Epidemiol 1999 6: 279–290.

    CAS  Article  Google Scholar 

  24. 24

    Mohan M, Sperduto RD, Angra SK et al. India-US case-control study of age-related cataracts Arch Ophthal 1989 107: 670–676.

    CAS  Article  Google Scholar 

  25. 25

    Leske MC, Chylack LT Jr, Wu SY . The Lens Opacities Case Control Study Group. The lens opacities case control study: risk factors for cataract Arch Ophthal 1991 109: 244–251.

    CAS  Article  Google Scholar 

  26. 26

    Willett WC, Stampfer MJ, Colditz GA et al. Dietary fat and the risk of breast cancer New Engl J Med 1987 316: 22–28.

    CAS  Article  Google Scholar 

  27. 27

    Rimm EB, Giovannucci EL, Willett WC et al. Prospective study of alcohol consumption and risk of coronary heart disease in men Lancet 1991 338: 464–468.

    CAS  Article  Google Scholar 

  28. 28

    Panel NOEIE. Clinical Guidelines on the Identification, Evaluation and Treatment of Overweight and Obesity in Adults—the Evidence Report Obes Res 1998 6: 51S–209S.

    Article  Google Scholar 

  29. 29

    Willett W, Stampfer MJ, Bain C et al. Cigarette smoking, relative weight, and menopause Am J Epidemiol 1983 117: 651–658.

    CAS  Article  Google Scholar 

  30. 30

    Rimm EB, Stampfer MJ, Colditz GA, Chute CG, Litin LB, Willett WC . Validity of self-reported waist and hip circumferences in men and women Epidemiology 1990 1: 466–473.

    CAS  Article  Google Scholar 

  31. 31

    Troy LM, Hunter DJ, Manson JE, Colditz GA, Stampfer MJ, Willett WC . The validity of recalled weight among younger women Int J Obes Relat Metab Disord 1995 19: 570–572.

    CAS  Google Scholar 

  32. 32

    Pirie P, Jacobs D, Jeffery R, Hannan P . Distortion in self-reported height and weight data J Am Diet Assoc 1981 78: 601–606.

    CAS  Google Scholar 

  33. 33

    Palta M, Prinieas RJ, Berman R, Hannan P . Comparison of self-reported and measured height and weight Am J Epidemiol 1982 115: 223–230.

    CAS  Article  Google Scholar 

  34. 34

    Brown L, Rimm EB, Seddon JM et al. A prospective study of carotenoid intake and risk of cataract extraction in US men Am J Clin Nutr 1999 70: 517–524.

    CAS  Article  Google Scholar 

  35. 35

    Chasan-Taber L, Willett WC, Seddon JM et al. A prospective study of carotenoid and vitamin A intakes and risk of cataract extraction in US women Am J Clin Nutr 1999 70: 509–516.

    CAS  Article  Google Scholar 

  36. 36

    Maraini G, Pasquini P, Sperduto RD et al. Risk factors for age-related cortical, nuclear, and posterior subcapsular cataracts Am J Epidemiol 1991 133: 541–553.

    Google Scholar 

  37. 37

    Mares-Perlman JA, Brady WE, Klein BE et al. Diet and nuclear lens opacities Am J Epidemiol 1995 141: 322–334.

    CAS  Article  Google Scholar 

  38. 38

    D'Agostino RB, Lee ML, Belanger AJ, Cupples LA, Anderson K, Kannel WB . Relation of pooled logistic regression to time dependent Cox regression analysis: the Framingham Heart Study Stat Med 1990 9: 1501–1515.

    CAS  Article  Google Scholar 

  39. 39

    Marshall RJ, Chisholm EM . Hypothesis testing in the polychotomous logistic model with an application to detecting gastrointestinal cancer Stat Med 1985 4: 337–344.

    CAS  Article  Google Scholar 

  40. 40

    Dubin N, Pasternack BS . Risk assessment for case-control subgroups by polychotomous logistic regression Am J Epidemiol 1986 123: 1101–1117.

    CAS  Article  Google Scholar 

  41. 41

    DerSimonian R, Laird N . Meta-analysis in clinical trials Control Clin Trials 1986 7: 177–188.

    CAS  Article  Google Scholar 

  42. 42

    Bochow TW, West SK, Azar A, Munoz B, Somemer A, Taylor HR . Ultraviolet light exposure and risk of posterior subcapsular cataracts Arch Ophthal 1989 107: 369–372.

    CAS  Article  Google Scholar 

  43. 43

    Hiller R, Sperduto RD, Podgor MJ et al. Cigarette smoking and the risk of development of lens opacities. The Framingham studies Arch Ophthal 1997 115: 1113–1118.

    CAS  Article  Google Scholar 

  44. 44

    Ford ES . Body mass index, diabetes and C-reactive protein among US adults Diabetes Care 1999 22: 1971–1977.

    CAS  Article  Google Scholar 

  45. 45

    Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB . Elevated C-reactive protein levels in overweight and obese adults JAMA 1999 282: 2131–2135.

    CAS  Article  Google Scholar 

  46. 46

    Hak AE, Stehouwer CD, Bots ML et al. Associations of C-reactive protein with measures of obesity, insulin resistance, and subclinical atherosclerosis in healthy, middle-aged women Arterioscler Thromb Vasc Biol 1999 19: 1986–1991.

    CAS  Article  Google Scholar 

  47. 47

    Cushman M, Yanez D, Psaty BM et al. Association of fibrinogen and coagulation factors VII and VIII with cardiovascular risk factors in the elderly Am J Epidemiol 1996 143: 665–676.

    CAS  Article  Google Scholar 

  48. 48

    Schaumberg DA, Ridker PM, Glynn RJ, Christen WG, Dana MR, Hennekens CH . High levels of plasma C-reactive protein and future risk of age-related cataract Ann Epidemiol 1999 9: 166–171.

    CAS  Article  Google Scholar 

Download references


Supported by grants CA40356, T32 ES07069, DK46200, EY09611 and EY12269 from the National Institutes of Health.

Author information



Corresponding author

Correspondence to JM Weintraub.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Weintraub, J., Willett, W., Rosner, B. et al. A prospective study of the relationship between body mass index and cataract extraction among US women and men. Int J Obes 26, 1588–1595 (2002).

Download citation


  • cataract
  • obesity
  • diabetes

Further reading


Quick links