Abstract
Greater body weight and fat mass have been associated with higher serum parathyroid hormone levels and a higher prevalence of primary hyperparathyroidism (P-HPTH) in women. However, prospective studies to evaluate whether greater body size associates with a higher incidence of developing P-HPTH have not been reported. We investigated whether greater body size was independently associated with a higher risk for developing P-HPTH in women. We conducted a prospective cohort study of 85,013 female participants in the Nurses' Health Study I followed for up to 26 years. Body size was measured via multiple metrics: weight, body-mass index (BMI), and waist circumference (WC). Weight and BMI were assessed every two years from 1986 to 2012, and WC was assessed in 1986, 1996, and 2000. Detailed dietary and demographic exposures were quantified via validated biennial questionnaires. Incident cases of P-HPTH were confirmed by individual medical record review. Cox proportional hazards models were used to evaluate whether WC, weight, and BMI were independent risk factors for developing P-HPTH. Models were adjusted for demographic variables, comorbidities, medications, intakes of calcium and vitamin D, and exposure to ultraviolet light. We confirmed 491 incident cases of P-HPTH during 2,128,068 person-years of follow-up. The multivariable adjusted relative risks for incident P-HPTH increased across quartiles of WC: Q1: ref, Q2: 1.34 (0.97, 1.86), Q3: 1.70 (1.24, 2.31), Q4: 2.27 (1.63, 3.18), P-trend<0.001. Similarly, the multivariable adjusted risks for incident P-HPTH increased across quartiles of weight: Q1: ref, Q2: 1.23 (0.92, 1.65), Q3: 1.63 (1.24, 2.14), Q4: 1.65 (1.24, 2.19), P-trend<0.001. A similar but statistically non-significant trend was observed across quartiles of BMI (P-trend=0.07). In summary, body size may be an independent and modifiable risk factor for developing P-HPTH in women.
Keywords: Primary hyperparathyroidism, parathyroid hormone, weight, waist circumference, body mass index
Introduction
Primary hyperparathyroidism (P-HPTH) is a relatively common condition that predominantly affects women and is associated with risks to their skeletal, renal, cardiovascular, and cognitive health 1-7. Despite its prevalence and established adverse consequences, our understanding of modifiable risk factors for developing P-HPTH is limited.
We have previously reported findings from prospective cohort studies identifying independent risk factors for developing P-HPTH, including lower calcium intake8, lower physical activity9, a diagnosis of hypertension and the use of loop-diuretics 10. Body size and weight are commonly implicated risk factors but these have not been longitudinally investigated.
In individuals without P-HPTH, higher body weight (specifically fat mass) has been independently associated with higher serum parathyroid hormone (PTH) levels11-14. Higher PTH with greater body size is often considered to be due to lower vitamin D bioavailability15; however, prior studies have suggested that the association between greater body size and higher PTH may be independent of 25-hydroxyvitamin D (25[OH]D) levels11,12. Further, multiple studies have shown that women with P-HPTH are heavier than eucalcemic controls without P-HPTH, raising to question whether body weight and size may increase the risk for developing P-HPTH16-18.
We hypothesized that greater body size (waist circumference, weight, and body-mass index) may be independent risk factors for developing P-HPTH in women. We evaluated this hypothesis by conducting a large prospective cohort study including more than 85,000 women followed for up to 26 years.
Methods
Study population
The Nurses' Health Study I (NHS) is an ongoing, national, prospective cohort study which began in 1976, enrolling 121,700 female registered nurses between 30 and 55 years of age. The cohort is followed by questionnaires mailed every two years that ask about lifestyle practices and newly diagnosed diseases. The follow-up of participants exceeds 90% of eligible person-time. Our analysis included 85,013 women who answered the 2008 questionnaire, which assessed lifetime history of P-HPTH, and had questionnaire assessment of weight, height, and waist circumference. All of these participants had data on weight, and 66,933 of the participants had data on waist circumference. The study protocol was approved by the Brigham and Women's Hospital institutional review board.
Assessment of Body Size
Information on weight and height was obtained on the baseline questionnaire (1976), and self-reported weight was updated every two years. The baseline questionnaire also asked about weight in early adulthood (age 18 years). Body-mass index (BMI) was calculated as the weight in kilograms divided by the square of height in meters. Self-reported weight has been demonstrated to be valid in NHS.19 Self-reported weights from 140 NHS participants were highly correlated with values obtained by technicians who visited the participants at home (r = 0.97).19
Waist circumference was reported on the 1986, 1996, and 2000 questionnaires. Study participants were instructed to measure their waist circumference at the level of the navel. If a tape measure was not available, participants were instructed to leave the question blank. Participants also were instructed to perform the measurements while standing, and to avoid measuring over bulky clothing. Participants reported their waist circumference to the nearest ¼ inch. Self-reported measures of waist size have been demonstrated to be valid; the correlation coefficient between self-reported waist circumference and measurement obtained by technicians sent to the homes of NHS participants was 0.89.20
Assessment of Dietary Intakes
To assess participants' diet, we used semi-quantitative food frequency questionnaires (FFQ) that asked about the average intake of more than 130 individual food items and 22 individual beverages during the previous year. The participants were asked to complete food frequency questionnaires in 1986 and every four years thereafter. Intake of specific dietary factors was computed from the reported frequency of consumption of each specified unit of food and from US Department of Agriculture data on the content of the relevant nutrient in specified portions. The FFQ also asked about the use of calcium supplements, vitamin D supplements, and multivitamins. Users of individual vitamin supplements were asked to provide the amount and frequency of use. The multivitamin users were asked to name the specific brand and the frequency of use. Intake of each vitamin or mineral was calculated by the frequency of intake multiplied by composition. The food frequency questionnaire has been extensively validated.21,22
Assessment of Non-Dietary Exposures
Age, race, smoking status (never, past, current), history of diabetes, hypertension, osteoporosis, chronic kidney disease, heart failure, menopausal status,23 and postmenopausal hormone use were ascertained from the biennial questionnaires. The use of medications (including those used to treat hypertension and osteoporosis) was updated every two years.
Biennial questionnaires also asked about the average time per week spent in the previous year on recreational and outdoor activities, including walking, jogging (slower than 10 min/mile), running (10 min/mile or faster), bicycling, calisthenics or exercise machines, tennis, racquetball or squash, lap swimming, weight lifting and outdoor work. A metabolic equivalent (MET) score was assigned for each activity, which is the metabolic rate associated with that particular exercise in comparison with the resting rate. MET scores for each activity were multiplied by the reported hours spent per week and summed to obtain a MET-hour score, with one MET corresponding to about 1 kcal per kilogram of body weight per hour. This assessment of physical activity has been previously validated against physical activity diaries in a similar cohort (r=0.79).24
Average annual ultraviolet B (UV-B) radiation was assessed. Higher levels of UV-B exposure were associated with higher serum 25(OH)D levels in the NHS cohort.25 UV-B flux is a composite measure of mean UV-B radiation level reaching the earth's surface that takes into account factors such as latitude, altitude and cloud cover, based on state of residence.25
Direct measurements of serum 25(OH)D were not available in most NHS study participants. However, predicted 25(OH)D levels were derived based on calculations developed from a subset of NHS participants with measured circulating 25(OH)D.25 Predicted 25(OH)D levels were calculated using the following factors that were associated with measured 25(OH)D in NHS: race, UV-B flux, intakes of dietary and supplemental vitamin D, BMI, physical activity, post-menopausal hormone use, and alcohol intake. The model used to generated predicted 25(OH)D in NHS has been shown to account for 33% of the total variability of measured 25(OH)D.25
Assessment of Incident P-HPTH
Participants were asked about a diagnosis of hyperparathyroidism on the 2006, 2008, 2010, and 2012 questionnaires. To confirm the diagnosis and distinguish P-HPTH from non-primary forms of hyperparathyroidism, we requested medical records of all participants who gave consent. For diagnoses before 2008, we confirmed cases of P-HPTH using the conservative criteria of an elevated serum concentration of calcium (≥10.6 mg/dL) with a concomitantly high or insufficiently suppressed PTH (> 50 pg/mL), and/or a pathology report indicating surgical resection of an adenomatous or hyperplastic parathyroid gland in the context of medical records indicating a diagnosis of P-HPTH. After 2008, we expanded the biochemical criteria for P-HPTH to include milder cases. These confirmatory criteria included a self-report and physician report of a diagnosis of P-HPTH in addition to one of the following: 1) serum calcium greater than the upper limit of normal (per the measuring laboratory's reference range) and serum PTH greater than the 50th percentile of reference range; 2) serum calcium within 0.2 mg/dL of the upper limit of reference range, and PTH greater than the upper limit of reference range, and 25(OH)D > 20 ng/mL, and estimated glomerular filtration rate (eGFR) >= 60 ml/min/1.73 m2.
Of the medical records that were reviewed, we confirmed P-HPTH in > 80% of participants. Self-reports were rejected after medical record review for a variety of reasons, most commonly secondary hyperparathyroidism from vitamin D deficiency or reduced eGFR, misreporting of a thyroid disorder as hyperparathyroidism, or insufficient data to confirm the diagnosis of P-HPTH.
Statistical Analyses
The study design was prospective; information on body size was collected before the diagnosis of P-HPTH. Participants with a history of P-HPTH at baseline were excluded. For each participant, we counted person-time of follow-up from the date on which the 1986 biennial questionnaire was returned to the date on which P-HPTH was diagnosed or death occurred, or May 31st, 2012, whichever occurred first. We allocated person-time of follow-up according to exposure status, beginning at baseline and updated every two-year follow-up period.
We used Cox proportional hazards regression to calculate relative risks. Waist circumference, weight, and BMI were categorized into quartiles, with quartile 1 serving as the reference category. Analyses were also repeated using categories of waist circumference, weight, and BMI. To test for linear trend, we used the median values in each category as a continuous variable in Cox models.
To control as finely as possible for confounding by age, we stratified the analysis jointly by age in months at the start of follow-up and calendar year of the current questionnaire cycle. Covariates considered in multivariable analyses were race (white or non-white), physical activity (quintiles),9 smoking status (past, current, never), menopausal status (pre or post), postmenopausal hormone use (yes or no), physical examination in prior two years, bisphosphonate use (yes/no), supplemental calcium intake (none, 1-500, >500 mg/day),8 intakes of total vitamin D and total vitamin A (quintiles), alcohol intake (none, 0.1-4.9, 5-14.9, ≥15 g/day), dietary intakes of calcium,8 magnesium, and protein (quintiles), UV-B radiation flux (quartiles), predicted 25(OH)D levels, and self-reported history of hypertension,10,26 diabetes, chronic kidney disease, congestive heart failure, or osteoporosis. Participants with missing waist circumference data were included in regression models as “missing” variables. No cases of P-HPTH occurred in participants with missing weight.
We calculated 95% confidence intervals for all relative risks. All P values were two tailed.
Results
Study Population Characteristics
We confirmed 491 incident cases of P-HPTH that developed during 2,128,068 person-years of follow-up. Four participants with incident P-HPTH lacked height data, which precluded calculation of BMI. There were 92 cases of incident P-HPTH without waist circumference data.
Baseline characteristics of the study population in 1986 by quartiles of waist circumference are presented in Table 1. Compared with women with smaller waist circumference, women with larger waist circumference were older, had higher BMI, greater weight, lower levels of physical activity, higher rates of prevalent hypertension and diabetes, lower supplemental calcium intake, lower ultraviolet exposure, and lower predicted 25(OH)D levels. Intakes of dietary calcium and protein, as well as menopausal status, were similar across quartiles of waist circumference. The characteristics of the entire eligible study population with waist circumference measurements during longitudinal follow-up are presented in Supplementary Table 1. Similar data for categories of waist circumference are presented in Supplementary Table 2.
Table 1.
Baseline characteristics of the study population in 1986 according to quartiles of waist circumference.+
| Waist Circumference | ||||
|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | |
|
| ||||
| Range (cm) | ≤68.6 | 68.7-76.2 | 76.3-86.4 | ≥86.5 |
| N | 9171 | 9603 | 13644 | 9289 |
| Age (y)* | 49.8(6.7) | 51.4(6.9) | 53.0(6.8) | 53.6(6.8) |
| Waist circumference (cm)* | 66.1(21.9) | 72.3(1.3) | 79.5(2.7) | 93.7(7.9) |
| Body mass index (kg/m²) | 21.0(1.9) | 22.5(1.9) | 24.7(2.6) | 29.9(4.6) |
| Weight (kg) | 57.7(9.8) | 63.5(10.8) | 69.6(12.5) | 80.4(16.6) |
| Physical activity (METS/wk)a | 19.4(25.3) | 16.3(20.1) | 14.1(19.5) | 11.1(17.7) |
| White race, % | 94.8 | 95.0 | 94.3 | 94.5 |
| Smoking status, % | ||||
| Never, % | 47.3 | 46.5 | 46.7 | 47.2 |
| Past, % | 32.7 | 35.0 | 35.9 | 36.8 |
| Current, % | 19.9 | 18.4 | 17.4 | 15.9 |
| Alcohol (gm) | 6.4(10.0) | 6.7(10.4) | 6.4(10.6) | 4.9(10.0) |
| Dietary Calcium Intake (mg) | 717 (252) | 722 (250) | 720 (247) | 720 (252) |
| Supplemental Calcium Intake (mg/d) | 411 (443) | 390 (436) | 359 (426) | 322 (418) |
| Calcium supplement use, % | 63.3 | 61.3 | 57.8 | 53.4 |
| Daily Vitamin D Intake (IU) | 363 (270) | 351 (252) | 337 (239) | 334 (239) |
| Predicted 25-hydroxyvitamin D (ng/mL)b | 29.3(3.2) | 28.8(3.3) | 27.8(3.3) | 25.2(3.9) |
| Daily Vitamin A Intake IU | 14590 (9278) | 13850 (7922) | 13275 (7500) | 12719 (7269) |
| Dietary Magnesium Intake (mg) | 310 (72) | 305 (69) | 300 (67) | 293 (67) |
| Dietary Protein Intake (gm) | 73 (12) | 74 (12) | 75 (12) | 75 (13) |
| UV-B radiation flux | 124 (25) | 123 (25) | 121 (24) | 120 (23) |
| History of hypertension, % | 12.1 | 14.8 | 21.5 | 35.8 |
| History of diabetes, % | 1.2 | 1.2 | 1.7 | 5.1 |
| Postmenopausal status, % | 65.0 | 65.8 | 66.0 | 67.4 |
| Postmenopausal hormone use, % | 22.0 | 20.9 | 19.2 | 14.9 |
| Self-reported osteoporosis, % | 3.8 | 3.9 | 4.0 | 4.4 |
| Self-reported Chronic Kidney Disease, %** | 0.0 | 0.0 | 0.0 | 0.0 |
| Self-reported Congestive Heart Failure, %*** | 0.8 | 0.8 | 1.0 | 2.1 |
| Bisphosphonate use, %*** | 7.4 | 6.0 | 4.5 | 2.6 |
Values are means(SD) or percentages and are standardized to the age distribution of the study population.
Values of polytomous variables may not sum to 100% due to rounding
Metabolic equivalents of recreational and leisure time activities.
Reference for equation25
Value is not age adjusted
Assessed in 1990
Assessed in 1998
The number of participants at baseline in 1986 (41,707) is lower than the total number used in our analysis because only 41,707 participants had data on waist circumference in 1986. More participants became eligible for the analysis when waist circumference became available during follow-up.
Waist Circumference and the Risk for Incident Primary Hyperparathyroidism
Larger waist circumference was associated with a higher risk of incident P-HPTH (Table 2). Compared with participants in the lowest quartile, the age-adjusted relative risk for P-HPTH in the highest quartile of waist circumference was 2.09 (95% CI: 1.57 to 2.79), with a significant trend across quartiles. These results were similar following additional comprehensive adjustments, as well as following additional adjustment for BMI and predicted 25(OH)D levels (Table 2). We conducted similar analyses using established categories of waist circumference (Supplementary Table 3). Compared with women in the lowest waist circumference category (< 72 cm), the multivariable-adjusted relative risk for P-HPTH for women in the highest waist circumference category (≥ 96 cm) was 2.52 (95% CI: 1.70 to 3.74). When waist circumference was dichotomized based on World Health Organization criteria as high (>80 cm) or low (≤80 cm), the multivariable-adjusted relative risk for P-HPTH for women with high waist circumference was 1.53 (95% CI 1.22 to 1.93). There was no statistically significant interactions between waist circumference, risk of incident P-HPTH, and predicted levels of serum 25(OH)D.
Table 2. Waist Circumference (Quartiles) and Risk for Incident Primary Hyperparathyroidism.
| Waist Circumference | |||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | P-trend | |
|
| |||||
| Range (cm) | ≤68.6 | 68.7-76.2 | 76.3-86.4 | ≥86.5 | |
| Median waist circumference (cm)a | 66.0 | 71.1 | 78.7 | 91.4 | |
| Incident Cases of P-HPTH | 73 | 79 | 112 | 135 | |
| Person-years of Follow-Up | 354,014 | 330,382 | 389,620 | 340,221 | |
| Age-adjusted RR | Ref | 1.32 (0.96,1.82) | 1.65 (1.23, 2.22) | 2.09 (1.57, 2.79) | <0.001 |
| Multivariate-adjusted Model 1 RR* | Ref | 1.29 (0.94, 1.78) | 1.57 (1.16, 2.12) | 1.96 (1.45, 2.65) | <0.001 |
| Multivariate-adjusted Model 2 RR** | Ref | 1.34 (0.97, 1.86) | 1.70 (1.24, 2.31) | 2.27 (1.63, 3.18) | <0.001 |
| Multivariate-adjusted Model 3 RR*** | Ref | 1.30 (0.95, 1.80) | 1.61 (1.19, 2.19) | 2.06 (1.52, 2.81) | <0.001 |
Data from baseline in 1986
Model 1: Multivariable model includes age, race, smoking status (past, current, never), physical activity (quintiles), menopausal status (pre or post), postmenopausal hormone use (yes or no), physical examination in prior two years, bisphosphonate use, history of hypertension, diabetes, chronic kidney disease, congestive heart failure, or osteoporosis, total vitamin D and vitamin A intake, supplemental calcium and alcohol intake, dietary intakes of calcium, magnesium, and protein, and UV-B radiation flux.
Model 2: Multivariable model includes adjustment for all variables in Model 1 and also BMI
Model 3: Multivariable model includes adjustment for all variables in Model 1 and also predicted 25(OH)D levels
Note: Waist circumference values for the year 2000 are shown; however waist circumference was updated over the course of the study.
Weight and the Risk for Incident Primary Hyperparathyroidism
Higher body weight was also associated with a higher risk of incident P-HPTH (Table 3). When compared with participants in the lowest quartile of weight, the age-adjusted relative risk for P-HPTH in the highest quartile was 1.68 (95% CI 1.28 to 2.20), with a significant trend across quartiles (Table 3). These results were similar following additional comprehensive adjustments. We also observed similar trends when examining weight as a categorical variable (Supplementary Table 3). The association between self-reported weight gain since early adulthood (age 18) and risk was not statistically significant. The association between weight loss and incident P-HPTH was not investigated because the number of women who lost weight over time was too small.
Table 3. Weight (Quartiles) and Risk for Incident Primary Hyperparathyroidism.
| Weight | |||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | P-trend | |
|
| |||||
| Range (kg) | ≤59.0 | 59.1-67.6 | 67.7-78.0 | ≥78.1 | |
| Median weight (kg) | 54.4 | 63.5 | 72.6 | 87.1 | |
| Incident Cases of P-HPTH | 86 | 99 | 154 | 152 | |
| Person-years of Follow-Up | 550,126 | 489,747 | 562,766 | 525,429 | |
| Age-adjusted RR | Ref | 1.22 (0.92, 1.63) | 1.62 (1.24, 2.11) | 1.68 (1.28, 2.20) | <0.001 |
| Multivariate-adjusted Model 1 RR* | Ref | 1.23 (0.92, 1.65) | 1.63 (1.24, 2.14) | 1.65 (1.24, 2.19) | <0.001 |
| Multivariate-adjusted Model 2 RR** | Ref | 1.25 (0.93, 1.68) | 1.70 (1.29, 2.24) | 1.78 (1.31, 2.40) | <0.001 |
Model 1: Multivariable model includes age, race, smoking status (past, current, never), physical activity (quintiles), menopausal status (pre or post), postmenopausal hormone use (yes or no), physical examination in prior two years, bisphosphonate use, history of hypertension, diabetes, chronic kidney disease, congestive heart failure, or osteoporosis, total vitamin D and vitamin A intake, supplemental calcium and alcohol intake, dietary intakes of calcium, magnesium, and protein, and UV-B radiation flux.
Model 2: Multivariable model includes adjustment for all variables in Model 1 and also predicted 25(OH)D levels
Note: Weight values for the year 2000 are shown; however weight was updated over the course of the study.
Body-Mass Index and the Risk for Incident Primary Hyperparathyroidism
Higher BMI was associated with a modest but non-statistically significant increase in risk for incident P-HPTH (Table 4). Although the age-adjusted trend across quartiles was statistically significant, when compared with participants in the lowest quartile of BMI, the relative risk for P-HPTH in the highest quartile was 1.27 (95% CI 0.99 to 1.63). The point estimates for risk were attenuated after multivariable adjustments and the trend was no longer statistically significant (Table 4). We also examined BMI in categories and observed similar trends (Supplementary Table 2). Continuous BMI was not associated with incident P-HPTH (P = 0.81).
Table 4. BMI (Quartiles) and Risk for Incident Primary Hyperparathyroidism.
| Body Mass Index | |||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | P-trend | |
|
| |||||
| Range (kg/m2) | ≤22.0 | 22.1-24.1 | 24.2-27.4 | ≥27.5 | |
| Median BMI (kg/m2)a | 20.8 | 23.0 | 25.6 | 30.3 | |
| Incident Cases of P-HPTH | 109 | 104 | 128 | 146 | |
| Person-years of Follow-Up | 511,979 | 511,723 | 511,404 | 511,063 | |
| Age-adjusted RR | Ref | 0.94 (0.71, 1.22) | 1.14 (0.88, 1.47) | 1.27 (0.99, 1.63) | 0.02 |
| Multivariate-adjusted Model 1 RR* | Ref | 0.92 (0.70, 1.20) | 1.09 (0.84, 1.42) | 1.16 (0.89, 1.53) | 0.14 |
| Multivariate-adjusted Model 2 RR** | Ref | 0.93 (0.71, 1.22) | 1.13 (0.86, 1.49) | 1.25 (0.93, 1.70) | 0.07 |
Data from baseline in 1986
Model 1: Multivariable model includes age, race, smoking status (past, current, never), physical activity (quintiles), menopausal status (pre or post), postmenopausal hormone use (yes or no), physical examination in prior two years, bisphosphonate use, history of hypertension, diabetes, chronic kidney disease, congestive heart failure, or osteoporosis, total vitamin D and vitamin A intake, supplemental calcium and alcohol intake, dietary intakes of calcium, magnesium, and protein, and UV-B radiation flux.
Model 2: Multivariable model includes adjustment for all variables in Model 1 and also predicted 25(OH)D levels
Note: BMI values for the year 2000 are shown; however BMI was updated over the course of the study.
Discussion
Primary hyperparathyroidism is a relatively common disorder that preferentially affects postmenopausal women1-5. Therefore, improving our understanding of potential modifiable risk factors for developing P-HPTH may support interventions to mitigate the incidence or severity of the disease and inform our insights into its pathogenesis. In this large prospective study including more than 85,000 women and 2.1 million person-years of follow-up, we observed a significantly higher risk for incident P-HPTH among participants with higher waist circumference and higher body weight. These associations were independent of many potential confounders, including concurrent medical conditions and medication use, previously identified risk factors for developing P-HPTH8-10, and reliable indices of calcium intake and predicted 25(OH)D status. Collectively, these findings suggest that body habitus and weight may be modifiable risk factors for developing P-HPTH in women.
It is important to note that while we observed strong and independent associations between body size and incident P-HPTH, the magnitude and nature of these associations differed. After multivariable adjustment, participants in the highest quartile of waist circumference (median 91.4 cm) had more than a 100% higher risk for developing P-HPTH when compared with those in the lowest quartile (median 66.0 cm). In contrast, participants in the highest quartile of body weight (median 87.1 kg) only had a 65% higher risk for developing P-HPTH when compared with those in the lowest quartile (median 54.4 kg), and BMI was not significantly associated with the risk for P-HPTH. These results suggest that an abnormal body habitus (i.e. central) may be more important in the risk for developing P-HPTH than total body weight. Further, given global increases in obesity27, in conjunction with recent evidence demonstrating an increasing prevalence of P-HPTH5, our findings suggest that the incidence of P-HPTH may be expected to progressively increase in the future.
The current findings extend the work of others. Prior studies in subjects without P-HPTH have observed a positive cross-sectional association between body size (and more specifically fat mass) and PTH levels11-14. Although greater body size and fat mass are inversely proportional to 25(OH)D levels, these previous observations were independent of 25(OH)D status, suggesting that the mechanism underlying higher PTH with greater body size may be more complex than simply invoking a secondary hyperparathyroidism due to adipose-tissue sequestration of vitamin D11,12. Our current observation associating higher body size with greater risk for developing P-HPTH was independent of predicted 25(OH)D levels. Although our predicted 25(OH)D calculations have been previously validated, they were not perfectly correlated with measured 25(OH)D levels, and we therefore cannot fully exclude the role of vitamin D status since direct 25(OH)D measurements were not available for most participants. Similarly, Reid et al. reported that individuals with P-HPTH have greater body weight and fat mass when compared with eucalcemic controls17, and subsequently conducted a large meta-analysis of 13 studies that confirmed this initial finding16. The directionality of this association could not initially be deciphered; however, Reid et al. later reported that in a small subset (n=38 with P-HPTH and n=30 eucalcemic controls) of their original study17, increased body weight preceded the diagnosis of P-HPTH18. In this regard, our large cohort study confirms and substantially extends these early findings by demonstrating that waist circumference and weight, measured and followed prior to the diagnosis of P-HPTH, are independently associated with the risk for developing P-HPTH.
The mechanism underlying the association between body size and incident P-HPTH was not directly investigated in our study and few prior studies provide sufficient insights to permit a refined speculation. Higher body size and adipose-tissue mass have been independently associated with diabetes and cardiovascular diseases27, risk for malignant cancers28, and mortality29-31, although the mechanisms explaining these associations are numerous and range from likely definitive to entirely speculative. The link between higher body weight and tumorigenesis has been ascribed to dysregulation of sex hormone metabolism, increased insulin and insulin-like growth factor signaling, and increased adipocytokines and inflammation28; however, the animal models that investigated these pathways did not detect (or specifically investigate) parathyroid dysplasia. To our knowledge, there are no studies implicating humoral factors secreted by adipose tissue that may stimulate parathyroid tissue or shared genetic or epigenetic predispositions that might link obesity with parathyroid dysplasia. Perhaps the most supportive link comes from the previously discussed human epidemiologic studies in participants without P-HPTH, where an independent association between greater body size and fat mass and higher PTH levels was observed, indicating that this relationship may precede the development of P-HPTH11,12. In this regard, basic and clinical studies focused on potential mechanisms to account for P-HPTH with greater body size or adipose tissue content are needed.
Limitations
This study has limitations. First, our study population was female and almost entirely white and therefore the findings may not be generalizable to men or other races; however, P-HPTH predominantly affects perimenopausal and older white women and therefore the findings focus on the highest-risk population5-7. Second, we were unable to exclude selection bias since we only included cases confirmed by medical record review and could not obtain medical records for all women who self reported P-HPTH. Third, many cases of P-HPTH may be asymptomatic and detected by routine bloodwork, which may be more frequent in women who pursue regular preventive care; however, we adjusted our analyses for regular physical examinations to account for this. Fourth, we did not have measurements of 25(OH)D levels on every participant, but we did adjust our models for factors that have been shown to predict 25(OH)D levels in NHS participants, including dietary and supplemental vitamin D intake based on validated food frequency questionnaires21,22,25, age, race, BMI, alcohol intake, and UV-B exposure25. We also adjusted our models for predicted 25(OH)D levels which are derived from a composite calculation that has been previously validated25. Fifth, we did not have measurements (such as dual-energy X-ray absorptiometry or computed tomography) to specifically measure fat mass; however, the concordance in our findings between body weight and waist circumference provide consistency and suggest that beyond weight alone, the distribution of weight (i.e. fat distribution) is likely to be a major contributor.
Summary
In summary, we conducted a large prospective cohort study of women and found that body waist circumference and weight were independent risk factors for developing P-HPTH. Given the increasing trends in global obesity, our findings may forecast a rising incidence of P-HPTH. Since increases in waist circumference and weight can often be prevented with lifestyle modifications and can be modified with weight loss (either medical or surgical), future studies should investigate whether these interventions can decrease the risk for developing P-HPTH.
Supplementary Material
Acknowledgments
We thank Dr. Henry Kronenberg from the Endocrine Unit at Massachusetts General Hospital for playing an important role in designing our study and his review of this manuscript. Funding was provided by NIH grants UM1-CA186107, CA087969, and DK099739. Anand Vaidya was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award Number K23HL111771, by the National Institutes of Diabetes and Digestive and Kidney Disease of the National Institutes of Health under Award Number R01 DK107407, and by Grant 2015085 from the Doris Duke Charitable Foundation. The National Institute of Digestive and Diabetes and Kidney Diseases of the National Institutes of Health supported Gary Curhan under Award Number K24DK091417 and Julie M Paik under Award Number K23DK100447. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Eric N. Taylor and Gary Curhan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors made substantial contributions to the interpretation of the results and writing of the manuscript.
Funding: This research and the authors were funded in part by grants from the NIH and Doris Duke Charitable Trust (see details in acknowledgments).
Footnotes
Disclosures: none
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