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. 2026 Aug 18;190(4):e70343. doi: 10.1002/ajpa.70343

Secular Trends in Height and Changes in Anthropometric Measures Among Adult Women in Nuñoa, Peru 1964–2018

Morgan K Hoke 1,2,✉, Nicole M Mayer 3, Adriana Wisniewski 1,2, Thomas L Leatherman 4
PMCID: PMC13484232  PMID: 42610773

ABSTRACT

Objectives

This study examines secular trends in body size among women in a high‐altitude Andean population in Nuñoa, Peru to evaluate the relative contributions of high‐altitude adaptation versus political‐economic and nutritional factors to the small body sizes previously observed in this population. It investigates changes in height, weight, and body composition across the 1960s, 1980s, and 2010s.

Materials and Methods

Anthropometric data were drawn from three cross‐sectional samples of adult women (ages 18–64) collected in the 1960s (n = 134), 1980s (n = 167), and 2010s (n = 169). Measures included height, weight, BMI, arm circumference, and triceps skinfolds. Participants were stratified by age (18–24; 25–64). ANCOVA and t‐tests were used to assess between‐period and within‐group differences.

Results

Women in the 2010s were significantly taller, heavier, and had higher BMI, arm circumference, and skinfolds than earlier cohorts, indicating a clear secular trend. No significant height differences were observed between the 1960s and 1980s. Increases in weight and BMI were driven primarily by women aged 25–64 years. Progressive increases in adiposity measures across all periods suggest shifting body composition. Age‐related differences in body size shift between the earlier periods and the 2010s.

Discussion

Findings demonstrate that improvements in socioeconomic conditions, healthcare, and diet have contributed significantly to increased body size, supporting a biocultural interpretation. Evidence of rising BMI and adiposity suggests that Nuñoa is undergoing an early‐stage nutrition transition, with declining undernutrition and emerging risks of overweight. Persistent short stature relative to national and Andean averages reflects ongoing structural inequalities despite recent gains.

Keywords: Andes, body size, high altitude, nutrition transition, secular trend

Highlights

  • Women in 2010s Nuñoa are taller, heavier, and have higher BMI and adiposity than earlier cohorts. No height gains occurred between the 1960s and 1980s. Changes reflect clear secular trends in body size.

  • Increases in weight and BMI are driven primarily by older women, with consistent rises in adiposity across cohorts. Age‐patterning of body size shifts in the 2010s, indicating changing life‐course dynamics.

  • Socioeconomic change, healthcare access, and diet explain body size increases. Findings support a biocultural explanation of previous short stature and suggest an early nutrition transition with emerging overweight.

1. Introduction

There has been significant scholarly debate around the sources of the overall short stature observed among Andean high‐altitude populations. Over the years, scholars have cited adaptation to the physiological challenges of life at high altitude as an important explanation for this relatively small adult stature (Frisancho 1969, 1970, 1977). Other scholars have pointed to the combined effects of the increased energetic costs of life at high altitude with the fact that most of the populations living at high altitude, particularly in the Andes, also experience a significant degree of marginalization and limited nutritional and healthcare resources (Hoke 2017; Hoke and Leatherman 2018; Leatherman 1998; Leonard 1989; Stinson 1982). One way to assess the role of broader political economic circumstances versus adaptation in trends in anthropometric measures such as height is to examine changes in these measures across time in a single location. The high‐altitude district of Nuñoa, Peru has been a site of anthropological research dating back to the 1960's and thus offers a unique opportunity to assess changes in adult anthropometrics across nearly 60 years.

In the 1960s, Nuñoa was a central site of research on human adaptation and the stresses of life in a high‐altitude environment (see Baker and Little 1976). Research during this period generated important insights on the role of developmental plasticity in human adaptation and suggested that delayed or slowed growth and reduced adult stature may have represented adaptive responses to the high‐altitude environment (Frisancho 1970, 1977, 1978; Frisancho et al. 1975; Frisancho and Baker 1970; Thomas 1973). In the 1980s, a new wave of research focused on the role of political economy in shaping the environment of Nuñoa, with scholars pointing increasingly to the role of nutritional and socioeconomic differences in producing the observed patterns of anthropometrics (Leatherman 1994, 1998; Leonard 1989). The civil conflict between the Sendero Luminoso and the Peruvian military prevented scholars from returning to the region for follow‐up work until the late 1990s. Relatively recently, anthropologists have returned to Nuñoa to assess the effects of social and political changes and the nutritional and epidemiological transitions that have accompanied them (Fisher 2015; Hoke 2017; Hoke and Leatherman 2018; Hoke and McCabe 2021; Schafrank et al. 2020).

The long history of anthropological research and careful documentation of anthropometrics in each of the study periods offers a unique opportunity to look for secular changes in body size in this population. Previous research has demonstrated marked improvement of growth among children across this time period in Nuñoa (Hoke and Leatherman 2018), though no similar work exists for adults. While it is not possible to parse out the role of altitude versus sociopolitical factors in producing short stature, a secular trend in increasing body size, specifically height, would point to the importance of factors beyond high altitude in producing the short stature observed among this population. The objective of this paper is to leverage existing data sets from the 1960s, 1980s, and 2010s to examine secular changes in height, weight, arm circumference, and triceps skinfolds among women from Nuñoa, Peru. Not only will such an examination shed some light on the relationship between adult stature and high altitude, but it will also illustrate the effects of ongoing nutritional and epidemiological transitions on body size and proportion more generally.

2. Materials and Methods

2.1. Background and Research Setting

Nuñoa is a high‐altitude district (from 3850 to 5500 masl) in the Southern Andes of Peru. Decades of anthropological research conducted in the region provide a unique opportunity to examine secular trends in human health within a single site (Baker and Little 1976). The district has a population of about 11,000 people (INEI 2016), about half of which live in the main town and capital of Nuñoa (4017 masl). The southern Peruvian Andes have long been home to a farming‐herding economy, where alpacas, llamas, and later sheep provided some meat for consumption and a source of income through the sale of wool and fiber. Potatoes and other tubers, and the Andean grains quinoa and cañihua are agricultural staples.

Prior to the 1970s, the landowning class, dominated by wool‐export haciendas, had control of land, animal production, the economy and politics. Much of the indigenous population was linked to these haciendas through feudal relations or sharecropping arrangements. Following an agrarian reform in 1968, much of this land was expropriated from the larger haciendas and reconstituted in state‐run cooperatives. For indigenous households able to become a socio on a cooperative, living conditions improved, but others were excluded, and the number of landless households and households with very limited access to land grew (Leatherman 1994, 1998; Luerssen 1994). Prior to the reform, 2% of the landholders controlled 61% of the land base, and after the reform three cooperatives controlled 60% of the land and employed about 25% of the rural population (Luerssen 1994).

In the late 1980s, the District of Nuñoa was drawn into the civil war of the Sendero Luminoso and Tupac Amaru revolutionary groups against the Peruvian state. The frustration around a failed agrarian reform, coupled with chronic severe poverty and its associated costs exacerbated by severe economic crisis on a national scale, represents important contexts for the revolution. The conflict arrived in the Department of Puno and Nuñoa in the late 1980s with significant consequences for food shortages and the overall health of the population (Leatherman and Thomas 2009). After the early 1990s, the conflict in Nuñoa abated and a period of recovery began.

In the past three decades, the demise of cooperatives and the redistribution of land to indigenous communities have increased economic well‐being slowly among agro‐pastoral producers. National economic growth (reaching 9%–10% between 2007 and 2009), policies of decentralization, and new development projects funded by government revenues from mining and international NGOs have improved rural livelihoods. While living conditions (electricity, potable water, etc.), literacy rates, and health statistics have improved significantly in the past 30 years, Nuñoa remains a site of significant poverty. Estimates indicate that the percentage of the population living in poverty ranges from 47%–61% (INEI 2015). Thus, at the time the 2018 data was collected, many families in Nuñoa were participating in several social aid programs, including a conditional cash transfer program known as JUNTOS, a nutritional supplement program for children and pregnant women called Vaso de Leche, and the national insurance program for impoverished segments of the population known as Seguro Integral de Salud or SIS.

A dietary assessment carried out in the town of Nuñoa in 2012 found that there is a greater variety of goods and foods (including vegetables, herbs, fruits, meat, and cheese) that were scarce or unavailable in the 1980s and 1990s, and dietary diversity in the town has expanded accordingly (Fisher 2015). Local infrastructural development projects have provided more wage work, artisan cooperatives have opened, and the ever‐growing dairy industry has improved both household income and economic stability (Hoke and McCabe 2021). These changes have also increased access to nutritional supplementation for households formerly living on the margins, setting the stage for examinations of how these transitions may affect adult body size.

2.2. Data Sources and Analytic Methods

The characteristics of the study samples examined from 1964 to 1966 are described by Frisancho (1976). This sample includes 142 women ages 18–64. The sample from 1984 to 1986 is made up of 176 women ages 18–64. Additional characteristics of this sample are provided by Leatherman et al. (1995). We were unable to obtain the original clean datasets used in publication from the 1960s and 1980s and the samples from those time periods are slightly different from those analyzed in the original papers (Frisancho 1976; Leatherman et al. 1995). For the data from the 1960s, we were able to obtain the coded anthropometric data from household surveys from the archives at the University of Massachusetts, Amherst. The data from the 1980s were sourced from printouts belonging to one of the original researchers and current authors of this article (TLL) and consist of data from both household surveys conducted in the 1980s. While data, and hence summary statistics, differ slightly from those published previously in these two cases, there were no statistically significant differences between our analyses and those of the original publications.

The data from the 2010s were collected in two different field seasons, one in 2014 and the other in 2018. The study in 2014 focused on mother/infant dyads and includes 127 non‐pregnant women ages 18–48. The 2018 data came from an investigation of gut health and microbiome that recruited 45 women ages 18–64, though the oldest participating women were 52 years of age. In both studies, measures were taken in triplicate and averaged. In both study years, investigations were completed with Institutional Review Board Approval (2014: Northwestern University & Universidad Peruana Cayetano Heredia; 2018: University of Pennsylvania). Informed consent was obtained from all participants in accordance with IRB protocols and all recorded measures were shared back with participants immediately.

For the 2014 data, height was measured to the nearest 1 mm using a Portstad Portable Stadiometer. Weight was measured to the nearest 100 g using a Lifesource digital scale and infant weight was measured to the nearest 100 g using a Seca 354 digital scale. Upper‐arm circumference was measured to the nearest 1 mm using a Teflon measuring tape and triceps skinfolds were measured to the nearest 0.5 mm using a Lange skinfold caliper (Beta Technology, Santa Cruz, CA). Three team members were responsible for conducting all anthropometric measures. To assure observer reliability, every 10th infant/mother dyad was measured independently by all three team members to control for inter‐observer errors in measurement. Mean difference, median difference, and technical error of measurement (Frisancho 1990) were calculated and demonstrated acceptable reliability when compared to various estimates of reliability and measurement error in past studies (Frisancho 1990; Leatherman et al. 1995; Lohman et al. 1992). BMI was calculated using height and weight measures and the formula (kg)/height (m)2.

In 2018, each type of measure was taken by the same study team member to reduce variability using the same type of equipment as the 2014 study. Team members were selected to conduct a measure based on a pretest assessment of measurement reliability and accuracy. Every 10th participant would be measured both before and after taking a survey by the same team member, and these two measures were used to assess technical error of measurement, which demonstrated acceptable reliability. All participants in the 2010s sample self‐identified as women. From all three study periods, observations missing either height or weight measures were dropped from the sample.

For all three study periods, Heymsfield's revised equations for muscle mass were used to calculate arm muscle area using mid upper arm circumference and triceps skinfolds (Heymsfield et al. 1982). Previous research has identified delayed maturation and prolonged periods of slow growth in this population (Frisancho and Baker 1970), as such, due to the potential of incomplete growth and uneven age distributions across samples, participants across all three study years were stratified by the age groups 18–24 and 25–64. We considered dividing the older age group into 25–44‐year‐olds and 45–64‐year‐olds but ultimately decided not to do so due to the small number of women (n = 6) over 45 years of age in the 2010s data set. To account for the effects of age and the differences in age composition across the different study periods, we conducted ANCOVAs controlling for age to assess whether height, weight, arm circumference, triceps skinfolds, and BMI varied across the years both for total samples and stratified by age category. Students t‐tests were used to test the significance of differences between age groups within each year.

3. Results

3.1. Between Year Difference in Anthropometric Measures

Table 1 provides the means and standard deviation of age, height, weight, BMI, arm circumference, triceps skinfold, and arm muscle area for women from each age category across all three study periods. The sample from the 2010s was significantly younger than the samples from the 1960s to 1980s (p < 0.0001) with the mean age for the 2010s sample being 28.2 years (7.33 SD) as opposed to 32.7 (11.39 SD) and 33.9 (12.0 SD) for the 1960s and 1980s, respectively. This difference was largely driven by the relatively larger percentage of women between 18 and 24 in the 2010s sample, 37.3% in comparison to 30.6% and 26.3%, and the relatively lower number of women over 45 years of age in the 2010s sample (n = 6). The mean ages for the 1960s and 1980s were not significantly different.

TABLE 1.

Anthropometrics across the three study periods by age category and full sample. All results are mean (SD) unless otherwise noted.

Year Age category (years) N (%) Age (years) Height (cm) Weight (kg) BMI (kg/m2) Arm circumference (cm) Triceps skinfold (mm) Arm muscle mass (cm2)
1960s 18–24 41 (30.6) 20.8 (2.06) 146.4 (6.14) 48.5 (6.65) 22.6 (2.24) 23.0 (1.98) 9.9 (4.25) 25.2 (5.65)
25–64 93 (69.4) 38.0 (9.72) 147.7 (5.22) 50.1 (6.75) 22.9 (2.55) 23.3 (1.74) 8.2 (4.0) 27.5 (6.08)
All Ages 134 32.7 (11.38) 147.3 (5.53) 49.6 (6.74) 22.8 (2.45) 23.1 (1.82) 8.7 (4.1) 26.9 (6.14)
1980s 18–24 44 (26.3) 20.7 (1.91) 147.7 (4.29) 51.1 (5.75) 23.4 (2.26) 24.9 (2.23) 14.3 (5.5) 26.6 (5.14)
25–64 123 (73.7) 38.6 (10.41) 147.4 (4.81) 50.5 (6.42) 23.2 (2.61) 24.0 (1.91) 11.5 (3.92) 26.9 (5.30)
All Ages 167 33.9 (12.0) 147.5 (4.67) 50.6 (6.24) 23.2 (2.52) 24.2 (2.03) 12.3 (4.57) 26.8 (5.24)
2010s 18–24 63 (37.3) 21.2 (1.80) 150.4 (4.49) 51.9 (7.67) 22.9 (2.81) 26.6 (2.05) 17.1 (3.95) 29.6 (5.78)
25–64 106 (62.7) 32.4 (6.11) 149.7 (5.63) 58.3 (9.0) 26.1 (4.0) 28.3 (2.61) 19.6 (4.72) 32.6 (7.96)
All Ages 169 28.2 (7.33) 149.9 (5.23) 55.9 (9.03) 24.9 (7.34) 27.6 (2.54) 18.7 (4.6) 31.5 (7.34)
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Figure 1 illustrates the difference in height among the three samples for the whole sample (1A & B), women 18–24 (1C), and women 25–64 (1D). ANOVA revealed that the women in the 2010s sample were significantly taller than those in the 1960s and 1980s (p < 0.0001) though there was no difference between the 1960s and 1980s samples. These patterns remained significant with stratifying by age though the level of significance decreased slightly (p < 0.0001 for 18–24 and p = 0.003 for 35–64). With the ANCOVA to control for the differences in age across the study periods, these patterns remained significant. For younger women, differences between the 2010s and the two earlier periods remained significant (1960s p < 0.0001; 1980s p = 0.009). For women ages 25–64 this was also the case (1960s p = 0.02; 1980s p = 0.004).

FIGURE 1.

FIGURE 1

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Height for women. (A) All women by year; (B) Full sample to compare between age groups. (C) Women ages 18–24; (D) Women ages 25–64. *** indicates significant difference from other groups at < 0.0001 level. ** at 0.001 level, * at 0.05 level. Brackets indicate if differences exist only between two groups.

Similarly with height, the full sample of women from the 2010s had significantly higher mean weight (55.9 kg, 9.03 SD, p = 0.0001) than those from the 1960s (49.6 kg, 6.74 SD) and 1980s (50.6 kg, 6.24 SD) with no significant differences between the 1960s and 1980s (see Figure 2). However, when stratifying by age group and controlling for age differences, some patterns emerge. ANCOVA reveals that for women aged 18–24, there is no significant difference in weight between the 2010 (mean = 51.9, 7.67 SD) and the 1980 (mean = 51.1, 5.75 SD) samples though the difference between the 1960 (mean 48.5, 6.65 SD) and 2010 sample is significant (p = 0.017). The difference between the 1980s and 1960s is trending toward significance but is not significant (p = 0.09). Among women between 25 and 64, the mean 2010s weight (58.3 kg, 9.0 SD) was significantly higher than the mean for the 1960s (50.1 kg, 6.75 SD, p < 0.0001) and the 1980s (50.5 kg, 6.43 SD, p < 0.0001), and the difference between the 1960s and 1980s was not significant.

FIGURE 2.

FIGURE 2

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Weight for women. (A) All women by year; (B) Full sample to compare between age groups. (C) Women ages 18–24; (D) Women ages 25–64. *** indicates significant difference from other groups at < 0.0001 level. ** at 0.001 level, * at 0.05 level. Brackets indicate if difference exists only between two groups.

For BMI (Figure 3A), women from the 2010s have significantly higher mean BMI (24.9, 7.34 SD, p < 0.0001) than those from either the 1960s (22.8, 2.45 SD) or the 1980s (23.2, 2.52 SD). However, when stratified by age group, it becomes clear that this difference is driven entirely by women between the ages of 25 and 64 in the 2010s, even when controlling for age differences (Figure 3C). The mean BMI for this group is 26.1 (4.0 SD) in contrast to 22.9 (2.55 SD) in the 1960s sample and 23.2 (2.61 SD) in the 1980s sample. There are no significant differences in BMI between years in the 18–24 age category, and in fact, the mean BMI recorded in the 1980s for this group was higher than the other years, though not significantly so.

FIGURE 3.

FIGURE 3

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BMI for women. (A) All women by year; (B) Full sample to compare between age groups. (C) Women ages 18–24; (D) Women ages 25–64. *** indicates significant difference from other groups at < 0.0001 level. ** at 0.001 level, * at 0.05 level. Brackets indicate if difference exists only between two groups.

The relationship between triceps skinfold and arm circumference across years is illustrated in Figure 4 for women from both age groups. Mean arm circumference for the 2010s sample (27.6 cm, 2.54 SD, p < 0.0001) was significantly larger than the other two groups. The sample from the 1980s (mean = 24.3, 2.03 SD, p = 0.007) was significantly larger than the mean arm circumference in the 1960s for all ages (mean = 23.1, 1.82 SD). The mean measure for triceps skinfolds in the 2010s sample is 18.7 mm (4.6 SD), and this was significantly different than that of the 1980s (mean = 12.3, 4.57 SD, p < 0.0001) and the 1960s (mean = 8.7, 4.1 SD, p < 0.0001). The mean skinfold for the 1980s was also significantly larger than that of the 1960s (p < 0.0001). When stratified by age group and controlling for age differences, these relationships remained consistent and highly significant. Figure 4 is a scatterplot which charts triceps skinfolds by arm circumference for the three complete samples. Included in the scatterplots are reference lines which indicate the mean measures for arm circumference and triceps skinfolds for each of the three samples.

FIGURE 4.

FIGURE 4

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A scatterplot which charts triceps skinfolds by arm circumference for the three complete samples. Included in the scatterplots are reference lines which indicate the mean measures for arm circumference and triceps skinfolds for each of the three samples. *** indicates significant difference from other groups at < 0.0001 level. ** at 0.001 level, * at 0.05 level. Brackets indicate if differences exist only between two groups.

Mirroring the relationships with arm circumference and triceps skinfold, arm muscle area was significantly larger among the 2010s sample (mean = 31.5, 7.34 SD) than either the 1960s (mean = 26.9, 6.14 SD, p = 0.0001) or the 1980s (mean = 26.8, 5.24 SD, p = 0.021). ANCOVA between the different age groups controlling for age revealed consistent significant differences between the 2010s and the other time periods, though no differences between the 1980s and 1960s were observed (Figure 5).

FIGURE 5.

FIGURE 5

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Arm muscle area for women by year and age group. *** indicates significant difference from other groups at < 0.0001 level. ** at 0.001 level, * at 0.05 level. Brackets are used to show differences within data sets between age categories. Larger asterisks indicate significance of differences between years.

3.2. Within Group Differences by Age

There were also some significant differences in weight, BMI, arm circumference, and triceps skinfolds between age groups within study years. For weight (Figure 2B), among women from the 2010s sample, there was a significant difference (p < 0.0001) between age groups where women ages 19–24 (mean = 51.9 kg, 7.67 SD) were much lighter than their 25–64‐year‐old counterparts (mean = 58.3 kg, 9.0 SD). There were no significant differences in weight between age groups in the 1960s or 1980s samples. Like weight, BMI showed a highly significant difference (p < 0.0001) between age groups in the 2010s sample (Figure 3B). Women between the ages of 19–24 (mean = 22.9 kg/m2, 2.81 SD) had a significantly lower BMI than women ages 25–64 (mean = 26.1 kg/m2, 4.0 SD). The difference in BMI between the 2010s and the other study years is driven entirely by women ages 25–64. There were no significant differences in BMI between age groups in the 1960s or 1980s samples.

For arm circumference (Figure 6), there were significant differences between 19–24‐year‐olds and 25–64‐year‐olds for both the 1980s sample (p = 0.005) and the 2010s samples (p = 0.006). In the 1980s, women ages 25–64 had significantly smaller arm circumferences (mean = 24.0 cm, 1.91 SD) than their younger counterparts (mean = 24.9 cm, 2.23 SD). In the 2010s, the relationship was opposite, with women ages 25–64 exhibiting significantly larger arm circumferences (mean = 28.3 cm, 2.61 SD) than their younger counterparts (mean = 26.6 cm, 2.05 SD). There were no significant differences in arm circumference between age groups in the 1960s sample. Finally, we observed significant differences between age groups across all three samples for triceps skinfolds (Figure 7). For both the 1960s (p = 0.03) and the 1980s (p = 0.0005) samples, women ages 19–24 had significantly larger triceps skinfolds than their older counterparts. However, among the 2010s sample (p = 0.0005), women ages 25–64 had significantly larger triceps skinfolds (mean = 19.6 mm, 4.72 SD) than women ages 19–24 (mean = 17.1 mm, 3.95 SD).

FIGURE 6.

FIGURE 6

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Arm circumference for women by year and age group. *** indicates significant difference between age groups within a year at < 0.0001 level. ** at 0.001 level, * at 0.05 level.

FIGURE 7.

FIGURE 7

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Triceps skinfolds for women by year and age group. *** indicates significant difference from other age groups within year at < 0.0001 level. ** at 0.001 level, * at 0.05 level.

4. Discussion

In general, we found that women in the 2010s were consistently and significantly larger on every anthropometric measure than women from the 1980s and 1960s. Women in the 1980s had significantly larger measures than women from the 1960s for arm circumference, triceps skinfolds, and arm muscle area, though the difference in arm circumference was less significant. When comparing differences across age groups, we identified important differences between women ages 18–24 and women ages 25–64, especially in the 2010s sample where there were significant differences in weight, BMI, arm circumference, triceps skinfolds, and arm muscle area between age groups with women ages 25–64 having significantly larger measures than their younger counterparts. For arm circumferences among women from the 1980s, relatively younger women had significantly larger measures than their older counterparts. For both women from the 1960s and 1980s, triceps skinfolds were significantly larger in women ages 19–24 than in women ages 25–64.

4.1. Secular Trend in Height

Our finding that women from the 2010s sample are significantly taller than women from the 1960s and 1980s is consistent with previous findings that showed a strong secular trend in height among children ages 3 to 14 in this area (Hoke and Leatherman 2018). Taken together, the findings suggest that with improving economic conditions, we see augmented growth among children who then go on to attain greater adult height. This secular trend is consistent with other studies that have shown increases in stature alongside major economic and infrastructural developments (Bogin 2013; Hauspie et al. 1997; Komlos 1994; Steckel 1995), suggesting that while there may be some underlying genetic influences of short stature in this population, environmental conditions such as poverty, lack of infrastructure, etc. are limiting physical growth and preventing people from reaching their genetic potential. This finding is also consistent with previous work in Nuñoa, which found that socioeconomic status and economic activities significantly influence growth trajectories among children (Hoke 2017, 2020; Hoke and Leatherman 2018; Leonard 1989).

With no significant differences in height between 19–24‐year‐olds and 25–64‐year‐olds in the 2010s, it appears that age category does not play an important role in driving this improvement. We might have expected that younger women would be taller than their older counterparts because they are more likely to have benefited from the improved conditions during their growth period. Instead, we see a wider range of measures in women aged 25–64 than in women aged 18–24, but no significant difference in the mean height. This larger distribution of heights may point to more significant variability in the conditions of development for this group. Because the older group is large in age range (25 years to 52 years), some of the younger women in this category may have benefited from economic improvements, while some of the older individuals may not, thus creating greater variability.

Despite this secular trend, adult women in Nuñoa are still significantly shorter than their peers in the rest of Peru and other Andean locations even when compared with rural and urban populations (see Figure 8). According to data from NCD RisC (see Rodriguez‐Martinez et al. 2020), in 2018, the mean height for women in Peru aged 19 was 154.0 cm, over 3.5 cm taller than the mean height in Nuñoa for women aged 18–24. Differences were even greater when compared with 19‐year‐old women from Bolivia, Ecuador, and Chile, where mean heights were 155.3, 154.9, and 159.3 cm, respectively. These disparities decrease in size but do not disappear when these group means are separated out into means for urban and rural populations. Even in comparison with rural populations from all four countries, the mean height of women in Nuñoa is notably lower. This disparity suggests that even with the infrastructural and economic improvements, the population of Nuñoa continues to experience key inequities that may manifest as growth disruption and reduced adult size.

FIGURE 8.

FIGURE 8

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Average height among females (cm) across Andean Countries using data from NHC RisC. Data for 19–24‐year‐old women from Nuñoa are presented along mean heights in 2020 for 19‐year‐old women from Peru, Bolivia, Ecuador, and Chile for the full population and for the urban and rural segments of the population.

4.2. Increasing Body Size and Signs of the Nutrition Transition

For weight and BMI, women from the 2010s are significantly heavier and have higher BMIs than women in the two earlier periods. Higher BMIs indicate that the higher weight is not solely due to increased height among women in the 2010s. The difference in weight and BMI appears to be driven primarily by women aged 25–64. There is a small but statistically significant difference in BMI between 18–24‐year‐olds in the 2010s and 1980s but not the 1960s, and no significant differences in weights among 18–24‐year‐olds across time points.

In the 2010s, 25–64‐year‐olds are heavier and have higher BMIs than in the 1960s and 1980s. They also have higher weights and BMIs than the 2010s 18–24‐year‐olds, a trend not observed in the 1960s or 1980s. Typically, body weight increases during early and middle adulthood (Chumlea et al. 2002; Guo et al. 1999; Kuk et al. 2009; Lei et al. 2006) before decreasing in older adulthood (Baumgartner et al. 1995; Carmelli et al. 1991). As such, one would expect higher weight and BMI in the 25–64 group compared to the 18–24 group. These differences were not observed in the 1960s and 1980s. One potential explanation is that women in the 1960s and 1980s experienced undernutrition and therefore did not gain weight with increasing age due to a lack of available nutrition and energy sources. The expected weight difference emerges in the 2010s, indicating that the population may be experiencing the early stages of a nutrition transition, in which the prevalence of undernutrition declines as nutritional availability increases, allowing older individuals to gain the expected weight with age (Popkin 1998, 1999). The nutrition transition and these observed changes align with Omran's epidemiological transition theory, which describes a decrease in the prevalence of infectious or communicable diseases and a simultaneous increase in non‐communicable diseases (Omran 2005). The epidemiological transition in Peru has followed the non‐Western transition model, characterized by a delayed onset but a more rapid progression, with persistently high fertility levels and a dual burden of disease (Omran 1998).

This finding is consistent with other studies globally (Inoue et al. 2018; Monteiro et al. 1995; Thompson et al. 2014). However, this pattern may not hold as the nutrition transition progresses to later stages that are no longer focused on famine reduction but instead highlight the emergence of nutrition‐related non‐communicable diseases, such as overweight and obesity, across the entire population, including at younger ages (Popkin 2004; Popkin et al. 2012). Patterns of increasing prevalence of obesity from the 1990s to 2010s in women aged 18–49 years old, with rates of obesity increasing most rapidly in more economically poor and rural populations, have also been observed (Santos et al. 2021). Similar findings related to demographic, epidemiological, and nutrition transitions have been observed in Chile, which underwent similar transitions starting in the 1980s (Albala et al. 2002; Albala and Vio 1995; Vio and Albala 2000). A study using data from NHANES found that across 1988–1994, more women gained weight or demonstrated stable obesity between midlife and late adulthood than between young adulthood and midlife (Sun and Du 2023), consistent with the pattern that we see in Nuñoa. However, in later study waves, the differences in weight gain decreased. By the 2013–2018 cohort, there is more weight gain among younger women, though rates of stable obesity remain higher for older women in the NHANES group (ibid). This shift in pattern in the NHANES data illustrates the sort of flip‐flop that can occur when younger populations are exposed to more years of nutrient‐poor, calorie‐dense food environments throughout development and thus start to show increasing levels of weight gain and BMI when compared with older groups. As such, we might expect to see this same pattern emerge in Nuñoa in future waves of research.

However, some additional light can be shed on these patterns when we look at measures of adiposity and muscle through arm circumference and triceps skinfolds. Arm circumference and triceps skinfold are the only measures for which there are significant differences between all 3 years for the full sample and when stratified by age group. The pattern shows increases in both arm circumference and triceps skinfolds across all three time points. However, when stratified by age group, we see that in the 1980s, younger women have larger arm circumference and skinfolds than older women, but this pattern reverses in the 2010s sample. This pattern is consistent with our BMI findings, suggesting that improving nutritional environments and standard of living may allow older individuals to express the expected changes to body composition associated with aging (Nubé et al. 1998; Welon et al. 2002). It is also important to note that some of the differences in these two age groups may be related to changes in body composition that occur with age and menopause. During the menopausal transition, hormonal changes, including decreases in estrogen levels, typically result in a slower metabolism and increased abdominal adipose tissue, along with a loss of lean muscle mass, which may be reflected in weight gain or increased BMI and risk for overweight or obesity (Fenton 2021; Greendale et al. 2019; Guthrie et al. 2004; Lovejoy et al. 2008; Pasquali et al. 1994; Shifren et al. 2014). It is important to note that the menopause transition does not fully account for the changes in weight and body composition associated with aging during this life stage (Matthews et al. 2001; Wang et al. 1994), a topic that remains understudied, especially in low‐ and middle‐income countries and diverse populations globally (Islam et al. 2025). These expected changes would also lead to an expected increase in weight and BMI for older women, 25–64 years, compared to the younger age group. These findings suggest that a more nuanced perspective on expectations of body composition change and aging may be warranted in populations undergoing nutritional and economic shifts in which patterns of work and leisure activities are changing along with dietary environments (Popkin 1998). Indeed, it is consistent with biocultural work that has long recognized the significant variation in the manifestations of aging processes across cultures (Lock 1998; Lock and Kaufert 2001; Melby et al. 2005; Melby and Lampl 2011; Sievert 2006, 2012).

4.3. Limitations

This study has some limitations that should be acknowledged. First is its sole focus on women. The 2010s data collection included a very small number of men, making a comparison across years impossible. We also do not have ethnic or racial data for the sample. Most people in Nuñoa identify either as Quechua or mestizo, but having additional information on people's ancestry might shed additional light on anthropometric patterns. We also have a relatively smaller sample of adults who were born in the early 2000's and may have benefitted from the emergence of dairying in the area during their early developmental window. If we had a larger sample of women who were born and spent their early years in a dairy‐rich environment, we might have seen even more significant differences in height among the 2010s sample.

Additionally, due to the nature of the studies in the 2010s, our sample size for that period was significantly younger than the other study periods. While we did control for sample differences in age in our analyses, our study could have benefited from including additional older women in the 2010s. More older participants would have allowed us to examine differences across three more even age groups rather than relying on the relatively large age grouping of women 25–64.

Finally, we cannot account for the potential effects of migration. It is possible that Nuñoa experienced either in‐ or out‐migration that may have affected anthropometrics. For example, it is common for families to move out of the town of Nuñoa to larger towns and cities as they gain more wealth. Individuals with greater mobility likely have more resources and may be taller consequently. Alternatively, as less well‐off families who had previously been living in small rural communities tend to move into the central town of Nuñoa, their SES increases. In both cases, the effect of this migration might lead us to underestimate the increases in body size, which ultimately supports our conclusions about secular trends being linked to socioeconomic, infrastructural, and nutritional improvements.

5. Conclusion

The long history of anthropological research in Nuñoa, Peru makes it possible to document the effects of long‐term socioeconomic and nutritional changes on health and wellbeing in this high‐altitude community. In our investigation of anthropometrics among women across three periods of time between the 1960s and 2010s, we find evidence of a clear secular trend with measures of height, weight, and adiposity increasing across time. Given the likely patterns of migration in this community, this change seems to be related to improving economic conditions, illustrating that the relatively short stature observed among Nuñoans is not only a result of exposure to high altitude but likely results from a synergistic interaction between the increased physiological costs of life and high altitude and the limited resources associated with socioeconomic marginalization and lack of infrastructure that characterized this region for hundreds of years.

While increases in height suggest improvements in nutritional environments, the increases in BMI and adiposity also likely signal the early stages of the nutritional transition, which is consistent with previous research in the area (Fisher 2015; Hoke 2020; Hoke and Leatherman 2018). Our results also illustrate how improving nutritional conditions can also change anthropometric patterns and their shift over the life course. Specifically, we see that women over the age of 25 are driving the increase in BMI and measures of adiposity in the 2010s, a reversal of the patterns observed in the 1960s and 1980s. Also importantly, we see that inequalities are persistent despite ongoing economic improvements. Nuñoans remain shorter than the national average and shorter than populations of other Andean countries. Our findings reinforce the need for a biocultural framework to interpret anthropometric patterns in populations where biological processes and environmental factors (such as high altitude) interact with political economic forces to shape human biology and health.

Author Contributions

Thomas L. Leatherman: conceptualization, writing – original draft, supervision, resources, data curation, methodology, writing – review and editing. Nicole M. Mayer: conceptualization, writing – review and editing, methodology, data curation, formal analysis, writing – original draft. Adriana Wisniewski: writing – original draft, writing – review and editing, resources. Morgan K. Hoke: conceptualization, investigation, funding acquisition, writing – original draft, methodology, writing – review and editing, formal analysis, project administration, data curation, supervision, visualization.

Funding

This work was supported by the Wenner‐Gren Foundation (Grant #8835). University of North Carolina at Chapel Hill. Mentor Grant, Population Studies Center, University of Pennsylvania. Fulbright Association ‐ Fulbright Hays Doctoral Dissertation Research Abroad Fellowship.

Disclosure

Microsoft Co‐pilot was used to generate an initial draft of the abstract and research highlights for this submission based on a review of the manuscript. The abstract and highlights were then checked and edited by the authors who take full responsibility for the content of the article. AI was not used in the analysis, main text drafting, or creation of tables and figures.

Ethics Statement

This paper relies on previously published data (from the 1960s and 1980s) as well as data collected more recently. Recent data collections were approved by the IRB committees at Northwestern University and the University of Pennsylvania, as well as the Ethics Committee at la Universidad Peruana Cayetano Heredia. Informed consent was obtained from all study participants in accordance with IRB protocols and any measures recorded were immediately shared with participants. Study findings were shared with community members through talks and radio programming following the study conclusion. Study findings were also shared with local government and health officials through reports and informes.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

The authors wish to acknowledge the people of Nuñoa, Peru for their generosity in participating in this research for so many years and the generations of scholars working in Nuñoa who made the use of this data possible.

Data Availability Statement

The deidentified data used in this study are publicly available through the Carolina Digital Repository.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The deidentified data used in this study are publicly available through the Carolina Digital Repository.

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