Computed tomography shows high fracture prevalence among physically active forager-horticulturalists with high fertility

^aAge at first birth is not normally distributed and is thus log-transformed (Std. β_{Age at 1st birth [years]}=0.110, p=0.019, controlling for age and fat-free mass).

^aLogging # births does not substantively affect results.

^bSubstituting other categorical measures of parity (e.g. quartiles) does not substantively affect results.

aLogging mean IBI does not substantively affect results.

^bMean IBI is not calculated for parous women with only one birth (n = 1), hence the reduction in sample size relative to Appendix 1—tables 1–2.

^aMedian split.

^bData are missing for five women.

^aMedian split.

^bData are missing for 24 women.

^cSome women either haven’t yet experienced menopause or cannot recall when they experienced menopause, hence the reduction in sample size for model 2.

^aUS data represent asymptomatic subjects from greater Los Angeles (described in Budoff et al., 2010). Briefly, 9585 subjects (43% female; mean age = 56) underwent coronary artery calcification (CAC) scanning for evaluation of subclinical atherosclerosis, after exclusion of participants with vertebral deformities or fractures. Subjects had no known bone disease.

^bMaximum age for Tsimane women and men = 91 and 94 years, respectively (US maximum age = 90 for both sexes).

^cUS means are weighted by sample sizes (shown in Table 1 of Budoff et al., 2010). This table reports age sub-groups in two-year intervals (e.g. 41–42, 43–44, etc.); for two-year intervals spanning multiple decades that overlap with the age categories shown in this table (i.e. 39–40, 49–50, 59–60 and 69–70), we assume that each year of the two-year interval contributes 50% of the sample.

^dP-value from a one-sample t test, including as the test value the weighted mean from Budoff et al. (2010).

^eAge-standardized means are shown to account for differences in age distributions across populations. We use the Tsimane adult age distribution (calculated from the 2015 THLHP census) as the standard. To calculate age-standardized means, means for each age category and population (i.e. unadjusted means for Tsimane and weighted means for US) are multiplied by the proportional representation of that age category in the 2015 THLHP census, and then summed across all age categories.

^aUS data are from two sources: 1) a subset of MESA study participants (described in Budoff et al., 2011), and 2) a subset of study participants in the greater Los Angeles area (described in Budoff et al., 2013). Briefly, the MESA cohort is a longitudinal, population-based study of 6814 adults (54% female; mean age = 62) free of clinical cardiovascular disease, representing six areas in the US: Baltimore, MD, Chicago, IL, Forsyth County, NC, Los Angeles, CA, New York, NY, and St. Paul, MN. Regarding participants from the greater Los Angeles area, data were collected among 4126 asymptomatic subjects (51% female; mean age = 64) who underwent CAC scanning for evaluation of subclinical atherosclerosis. MESA and Los Angeles-only subsets were first matched for age and sex with the Tsimane sample, and then merged to create a single US comparison sample.

^bFor women (n = 245 Tsimane and 246 US), sample sizes for ages 40–49 are 80 Tsimane and 77 US, for ages 50–59 89 Tsimane and 86 US, for ages 60–69 49 Tsimane and 60 US, and for ages 70+ 27 Tsimane and 23 US. For men (n = 261 Tsimane and 263 US), sample sizes for ages 40–49 are 77 Tsimane and 69 US, for ages 50–59 98 Tsimane and 108 US, for ages 60–69 56 Tsimane and 54 US, and for ages 70+ 30 Tsimane and 32 US.

^cMaximum age for Tsimane women and men = 91 and 94 years, respectively. Maximum age for US women and men = 92 and 91 years, respectively.

^dAge-standardized prevalences are shown to account for differences in age distributions across populations. We use the Tsimane adult age distribution (calculated from the 2015 THLHP census) as the standard. Prevalence for each age category and population is multiplied by the proportional representation of that age category in the 2015 THLHP census, and then summed across all age categories.

^**p≤0.01 *p≤0.05 ¶p≤0.1

^***p≤0.001 **p≤0.01 *p≤0.05 ¶p≤0.1

^aRepresents maximum possible sample size (i.e. no missing data).

^bP-value from Mann-Whitney U or χ² test.

^**p≤0.01 *p≤0.05 ¶p≤0.1

^aRepresents maximum possible sample size (i.e. no missing data).

^bP-value from Mann-Whitney U or χ² test.

^aFracture data are missing for one man with BMD and anthropometric data. We also omitted a man whose height (138.2 cm) was 4.3 SDs below the mean.

^***p≤0.001 **p≤0.01 *p≤0.05 ¶p≤0.1

^aModel parameters include BMD, age and fat mass (not height). Joint inclusion of both height and fat mass yields invalid estimates.

^bModel parameters include BMD (adjusted RR = 0.75, 95% CI: 0.61–0.92, p=0.006), age (adjusted RR = 0.84, 95% CI: 0.66–1.07, p=0.159) and height (not fat mass, n = 253).

^cModel parameters include BMD (adjusted RR = 1.21, 95% CI: 0.81–1.82, p=0.358), age (adjusted RR = 1.40, 95% CI: 0.92–2.14, p=0.116) and height (not fat mass, n = 253).

^aData were missing for various reasons (see supplementary text for details).

^b185 of the 245 participating women (76%) were post-menopausal. 12 women were post-menopausal but could not recall their age at menopause. The remaining 60 women (245-185) were either pre-menopausal (n = 36) or missing data regarding menopausal status (n = 24). Women were categorized as post-menopausal if they reported during THLHP medical exams not having experienced a menstrual cycle in the past year, and were neither pregnant nor lactating at the time of the study.

^***p≤0.001 **p≤0.01 *p≤0.05 ¶p≤0.1

^aData are missing for seven women.

^bData are missing for 26 women.

^cVariance inflation factors do not indicate a high degree of multicollinearity.

^***p≤0.001 **p≤0.01 *p≤0.05 ¶p≤0.1

^aData are missing for seven men.

^bData are missing for 33 men.

^cVariance inflation factors do not indicate a high degree of multicollinearity.