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. 2026 Mar 15;38(3):e70238. doi: 10.1002/ajhb.70238

Nutrition and Handgrip Strength Among Adults in Rural Matlab, Bangladesh

Gauri Naik 1, Victoria Durney 1, Emily Gray 1, JoceLynn Labossiere 1, Luisa Sanchez 1, Jenna Seafuse 1, Anjan Kumar 2, Farjana Haque 2, Tami Blumenfield 3, Siobhan Cully 4, Nurul Alam 2, Rubhana Raqib 2, Katherine Wander 1,✉, Mary K Shenk 5
PMCID: PMC12989739  PMID: 41834426

ABSTRACT

Objective

Handgrip strength (HGS) provides a dynamic indicator of overall strength and physical fitness. Population‐based investigations of anemia and iron deficiency as predictors of HGS are limited; most studies rely on elderly and/or clinical samples. We assessed iron deficiency, anemia, body mass index (BMI), and body fat percentage as predictors of HGS among a representative cross‐sectional sample of adult residents of Matlab, Bangladesh.

Methods

We collected HGS, anthropometric measurements, and whole blood specimens from a representative, population‐based sample of adults in Matlab. We used hemoglobin to identify anemia and soluble transferrin receptor to identify iron deficiency. We estimated mixed‐effects generalized linear models of HGS.

Results

Iron deficiency, but not anemia, was negatively associated with HGS among both men and women. Among men, but not women, underweight BMI was negatively associated with HGS, and overweight BMI was positively associated with HGS.

Conclusions

Iron nutrition is important for muscles' capacity for work and overall physical fitness, regardless of the presence of anemia. Among men (but not women) in this setting, BMI categories seem to capture differences in muscle tissue as well as adiposity.

Keywords: anemia, body mass index, iron deficiency, physical fitness, underweight

Body mass index was associated with grip strength among men (red) but not women (blue) in a random sample of adults in rural Matlab, Bangladesh, controlling for iron deficiency, hemoglobin, and age.

graphic file with name AJHB-38-e70238-g001.jpg

1. Introduction

Handgrip strength (HGS), defined as the maximum strength of both extrinsic and intrinsic hand muscles contracting and leading to hand joint flexion, provides a biomarker of muscle work capacity and physical fitness that is dynamic, non‐invasive, and inexpensive to assess (Mainous et al. 2015; Norman et al. 2011; Russell 2015). HGS affords researchers an opportunity to assess downstream outcomes of muscle mass and strength, like work capacity, that are important determinants of well‐being at the individual, household, and community levels.

HGS tends to decline with age, and weak HGS is more pronounced in those over 65 (Gi et al. 2020). HGS is generally associated with chronic disease severity, particularly cardiovascular disease and cancer (Bohannon 2019), and disease‐specific mortality (Leong et al. 2015; Rantanen et al. 2000). For example, a study of elderly patients hospitalized with pneumonia found that HGS was a strong predictor of outcomes 1 month after hospitalization, including death (Bohannon et al. 2004).

Nutrition impacts HGS (Aydik and Ertuğrul 2023; Norman et al. 2011). Specifically, loss of muscle mass resulting from inadequate dietary protein is associated with weaker HGS (Norman et al. 2011). In addition, results from multiple studies—summarized in Table 1—show that HGS is lower among those with anemia, especially in men (Gi et al. 2020; Lee and Chi 2023; Sutandyo et al. 2022), but see Joosten et al. (2016).

TABLE 1.

Publications to date regarding iron deficiency, anemia, and handgrip strength.

Publication Population and sample size Age range and sample type Predictor Observed association with HGS

Penninx et al. (2004)

DOI: 10.1111/j.1532‐5415.2004.52208.x

Italy

N: 11 008

Age: ≥ 65

Sample: population‐based

 Anemia Anemia was associated with lower HGS
Iron deficiency anemia Iron deficiency anemia was associated with lower HGS

Thein et al. (2009)

DOI: 10.1097/MD.0b013e31819d89d5

USA

N: 328

Age: ≥ 65

Sample: clinical (out‐patient)

 Hemoglobin concentration Hb was positively associated with HGS

Haslam et al. (2012)

DOI: 10.1111/jgs.12009

United States

N: 244

Age: 98–101

Sample: population‐based

 Anemia Anemia was associated with lower HGS

Yamada et al. (2015)

DOI: 10.6133/apjcn.2015.24.3.11

Japan

N: 202

Age: ≥ 60

Sample: population‐based women

 Hemoglobin concentration Hb was positively associated with HGS

Hirani et al. (2016)

DOI: 10.1093/gerona/glw055

Australia

N: 1566

Age: ≥ 70

Sample: population‐based

 Hemoglobin concentration Hb was negatively associated with “poor” HGS

Joosten et al. (2016)

DOI: 10.1186/s12877‐016‐0326‐y

Belgium

N: 220

Age: ≥ 70

Sample: clinical (acute geriatric ward admissions)

 Anemia, hemoglobin concentration None

Selvi Öztorun et al. (2018)

DOI: 10.1007/s41999‐018‐0065‐z

Turkey

N: 81

Age: ≥ 65

Sample: clinical (iron deficiency and iron deficiency anemia)

 Iron supplement treatment HGS increased ~6 months after start of treatment

Santos et al. (2019)

DOI: 10.1111/phn.12579

Brazil

N: 109

Age: 60–95

Sample: population‐based

 Anemia Anemia was associated with lower HGS

El Shemy et al. (2019)

DOI: 10.3923/pjbs.2019.214.219

Egypt

N: 100

Age: 4–6

Sample: clinical (cerebral palsy)

 Anemia Anemia was associated with lower HGS
Hemoglobin concentration Hb was positively associated with HGS

Fukushima et al. (2019)

DOI: 10.1177/1534735419842196

Japan

N: 60

Age: > 20

Sample: clinical (hematological malignancies)

 Hemoglobin tertile Hb was positively associated with HGS

Gi et al. (2020)

DOI: 10.1371/journal.pone.0218058

South Korea

N: 16 638

Age: ≥ 19

Sample: population‐based

 Anemia Anemia was associated with “weak” HGS; stronger association among men and elderly

de Araújo et al. (2020)

DOI: 10.1186/s12889‐020‐08504‐z

Brazil

N: 1016

Age: ≥ 60

Sample: population‐based

 Hemoglobin concentration Anemia was independently associated with low handgrip strength

Marzban et al. (2021)

DOI: 10.1186/s12877‐021‐02285‐9

Iran

N: 2426

Age: ≥ 60

Sample: population‐based

 Hemoglobin concentration Higher Hb was associated with higher HGS

Sutandyo et al. (2022)

DOI: 10.7759/cureus.25290

Indonesia

N: 3192

Age: ≥ 60

Sample: population‐based

 Anemia Anemia was associated with “weak” HGS

Lee and Chi (2023)

DOI: 10.1038/s41598‐023‐45985‐5

South Korea

N: 24 022

Age: ≥ 30

Sample: population‐based

 Anemia Anemia was associated with lower HGS; stronger among men
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Anemia—insufficient erythrocytes and hemoglobin to deliver oxygen to tissues (WHO 2011)—is most commonly, but not exclusively, due to iron deficiency (Warner and Kamran 2024). Iron is a key component of hemoglobin, binding oxygen for transport throughout the body. Animal models of iron deficiency anemia demonstrate its negative effects on endurance (K. J. Davies et al. 1982; S. Davies et al. 1984). In human studies, iron deficiency anemia has been shown to cause reductions in aerobic work capacity and endurance, while iron deficiency in general has been associated with higher energetic costs when performing low levels of voluntary physical activity (Haas and Brownlie 2001; Stugiewicz et al. 2016).

Iron deficiency affects skeletal muscle at multiple levels (Stugiewicz et al. 2016). In addition to its role in oxygen transport, iron within mitochondria is important for capturing energy from glycolysis; muscle cells are especially intensive in their demands for oxygen and iron because their function necessitates high energy turnover (Chaves 2008; Stugiewicz et al. 2016). There is thus good reason to expect independent effects of both anemia and iron deficiency on muscle strength (and thus, HGS). Existing studies have not attempted to distinguish the effects of iron deficiency and anemia. In addition, with two exceptions that consider large and representative samples of Korean adults, the majority of these studies focus on elderly and/or clinical populations, often those undergoing hospitalization or medical treatment, with limited generalizability to the broader population.

Understanding the impact of iron deficiency and anemia on HGS is complicated by the multifaceted nature of iron nutrition, which is affected by both the content and bioavailability of iron in foods; iron bioavailability can be enhanced by other micronutrients (e.g., vitamin C) and inhibited by other dietary components (e.g., calcium, phytate) (Hurrell and Egli 2010). Further, diets that produce iron deficiency and anemia are likely to be lower in one or more macronutrients, or overall energy, potentially resulting in other manifestations of malnutrition, such as underweight. Body mass index (BMI), or weight for height (kg/m2), is often negatively associated with anemia (Qin et al. 2013; Saad and Qutob 2022). HGS generally increases with BMI (Sutandyo et al. 2022); although BMI is widely employed as a means to identify undernutrition (underweight) and excess adiposity (overweight and obesity), it increases with both adipose and muscle tissue (Bailey and Ferro‐Luzzi 1995; Hall and Cole 2006; Saad and Qutob 2022). To the extent that BMI captures muscle tissue, we should expect higher BMI to be positively associated with HGS. We thus assessed the independent effects of these multiple facets of nutrition—anemia, iron deficiency, underweight, overweight, obesity, and body fat percentage—on HGS in a representative, population‐based sample of adults in Matlab, Bangladesh.

2. Material and Methods

2.1. Study Population

Matlab, Bangladesh, is a rural area south of the capital city of Dhaka. Through the 1980s, farming was a majority occupation in this region, with production for both subsistence and market sale. Currently, however, the area is undergoing increasing integration into global and regional markets that has reduced reliance on agriculture for both food and income. Men's economic activities include farming, wage labor, and small business; women often participate in farm work, but do not typically engage in paid labor. Most residents of Matlab now purchase a majority of their food, and while whole foods (rice, lentils, vegetables, fish, and meat) constitute the majority of the diet, there is increasing access to processed foods. Labor migration out of Matlab is common among reproductive‐aged men; migrants tend to be younger and healthier than those who remain (Alam et al. 2024).

In 2010, in collaboration with the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), we collected an age‐stratified random sample of 944 women between the ages of 20 and 65 from the Matlab Health and Demographic Surveillance System (HDSS) longitudinal research study. In 2017–18, 766 of the original participants, as well as 499 of their husbands, participated in a second wave of data collection, providing the data analyzed here. Most attrition between study phases was due to migration out of the study area (n = 154); the rest was due to death, disability, refusal, or prolonged travel. The mismatch between the number of women and men interviewed in each household reflects the real demography of Matlab, with high rates of labor migration out of the area by men.

2.2. Research Ethics Approval

This study was approved by the Pennsylvania State University Institutional Review Board and the icddr,b Ethical Review Committee and conforms to the standards laid out in the Declaration of Helsinki and the US Federal Policy for the Protection of Human Subjects. All participants provided written informed consent.

2.3. Anthropometry and Specimen Collection

Participants provided sociodemographic information via survey; ages were verified against HDSS date of birth records. We used a portable stadiometer (Seca 213) to estimate participants' standing height to the nearest 0.1 cm, and a portable digital scale with bioelectrical impedance (Tanita BC 545) to estimate participants' weight to the nearest 0.1 kg and body fat percent (BF%) to the nearest 0.1%. BF% estimates with this instrument have been validated against dual‐energy x‐ray absorptiometry (DEXA) (Wang and Hui 2015). We estimated participants' HGS to the nearest 0.1 kg using a portable digital hand dynamometer (Smedley 12‐0286). Participants were asked to grasp the dynamometer in one hand with an outstretched arm while standing and squeeze as hard as they could; the dynamometer displayed the highest force applied by the participant during each trial. We collected three trials of each hand for each individual in quick succession.

We collected capillary whole blood via finger stick with a sterile lancet. We immediately assessed one drop for hemoglobin (Hb) concentration (g/dL) with a portable hemoglobinometer (HemoCue 201+). Up to five additional drops were collected on Whatman #903 filter paper cards for dried blood spot (DBS) specimens. DBS were allowed to dry at ambient temperature in the icddr,b facility for up to 24 h then stored at −20°C.

2.4. Laboratory Analyses

We characterized iron deficiency with soluble transferrin receptor (sTfR), as this biomarker has been validated for estimation in DBS and is robust to inflammation when compared to alternatives such as ferritin (McDade and Shell‐Duncan 2004; Wander et al. 2009). DBS were evaluated for sTfR by enzyme immunoassay in icddr,b's Immunobiology, Nutrition, and Toxicology Laboratory using a commercially available kit (Ramco TFC‐94), modified for use with DBS as follows: one 3.2 mm disc of DBS specimen, equivalent to 1.5 μL serum (Hewawasam et al. 2018; Mei et al. 2001), was removed with a hole punch and eluted in kit assay buffer overnight. Eluent was then assayed without further dilution following kit instructions. Thirty‐five plates of sTfR were included in these analyses; the intraassay coefficient of variation (CV) was 3.48%, and the interassay CVs were 14.8% at low concentration and 9.5% at high concentration.

2.5. Statistical Analyses

Anemia was defined using the World Health Organization standards of Hb < 12.0 g/dL for women and Hb < 13.0 g/dL for men (WHO 2011). Consensus is lacking around the best sTfR cutpoint to identify iron deficiency (Frosch et al. 2018; Wander et al. 2009), so we identified iron deficiency by two definitions: sTfR ≥ 8.3 mg/L (Frosch et al. 2018; Phiri et al. 2009) and sTfR ≥ 5.0 mg/L (McDade and Shell‐Duncan 2004; Wander et al. 2009). We defined underweight as BMI < 18.5, lean as 18.5 ≤ BMI < 23, overweight as 23 ≤ BMI < 25, and obese as BMI ≥ 25, following WHO recommendations for Asian populations (Siddiquee et al. 2015). We excluded participants with missing or implausible values (N = 103) and those who were pregnant (N = 4) from these analyses.

We estimated mixed‐effects linear models, with individuals nested within households, to describe predictors of HGS. Predictors of interest were iron deficiency, anemia, BF%, and BMI category. Biomarkers of iron deficiency and anemia were evaluated as predictor variables in binary form, using the cutpoints described above, as well as in quartile and continuous forms. We considered sex and age as potential control variables. This approach accounted for household clustering and the potential for both linear and non‐linear associations between predictors and HGS. We used Stata 18 for all analyses.

3. Results

Complete data were available for 1158 participants: 728 women and 430 men (Table 2). HGS was higher among men (Figure S1). Anemia was more common among women (48%) than men (43%). Iron deficiency by the more conservative sTfR cutpoint of 8.3 mg/L was uncommon (4% of both women and men), while many participants exceeded the lower sTfR cutpoint for iron deficiency of 5.0 mg/L (52% of women and 47% of men). Underweight was more common among men (21% vs. 10%) while obesity was more common among women (38% vs. 19%).

TABLE 2.

Sample characteristics.

Women Men
Mean St dev Mean St dev
Grip strength (kg) 15.8 4.1 27.5 6.7
Age (years) 49.9 12.3 56.2 12.0
Body mass index (BMI; kg/m2) 23.6 4.2 21.5 3.7
Whole body fat (%) 30.9 7.6 18.9 6.8
Soluble transferrin receptor (sTfR; mg/L) 5.4 1.8 5.3 2.0
Hemoglobin (Hb; g/dL) 11.9 1.4 13.1 1.8
N % N %
Iron deficiency (sTfR ≥ 8.3 mg/L) 32 4.4 19 4.4
Iron deficiency (sTfR ≥ 5.0 mg/L) 382 52.5 203 47.2
Anemia 352 48.4 185 43.1
BMI categories
Underweight 73 10.0 90 20.9
Lean 267 36.7 202 47.0
Overweight 114 15.7 57 13.3
Obese 274 37.6 81 18.8
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HGS decreased with age (Table 3) and was negatively associated with iron deficiency by the higher sTfR cutpoint, but there was no association between HGS and iron deficiency by the lower sTfR cutpoint, sTfR by quartile, or continuous sTfR; conversely, anemia was unassociated with HGS, but there was an apparent positive association between Hb and HGS. We thus chose to use the binary iron deficiency (sTfR ≥ 8.3 mg/L) variable and the continuous Hb (g/dL) variable in subsequent models (Table 4). No interaction between iron deficiency and anemia was apparent (controlling for age and gender, p = 0.942 for anemia by iron deficiency using the 8.3 mg/L cutpoint; p = 0.694 for anemia by iron deficiency using the 5.0 mg/L cutpoint).

TABLE 3.

Iron deficiency and anemia variables as predictors of handgrip strength.

Predictor Coefficient 95% Confidence interval p
A. Anemia and iron deficiency as binary variables (high sTfR cutpoint)
Iron deficiency (sTfR ≥ 8.3 mg/L) −1.8 −3.1, −0.5 0.006
Anemia (Hb < 13 g/dL for men, Hb < 12 g/dL for women) −0.2 −0.8, 0.3 0.389
Age (years) −0.2 −0.2, −0.2 0.000
Gender (man) 12.9 12.3, 13.4 0.000
B. Anemia and iron deficiency as binary variables (low sTfR cutpoint)
Iron deficiency (sTfR ≥ 5.0 mg/L) −0.2 −0.8, 0.3 0.360
Anemia (Hb < 13 g/dL for men, Hb < 12 g/dL for women) −0.3 −0.9, 0.2 0.237
Age (years) −0.2 −0.2, −0.2 0.000
Gender (man) 12.8 12.3, 13.4 0.000
C. Hemoglobin and soluble transferrin receptor as ordinal variables
sTfR quartile 0.0 −0.2, 0.2 0.956
Hb quartile 0.2 −0.1, 0.5 0.170
Age (years) −0.2 −0.2, −0.2 0.000
Gender (man) 12.7 12.1, 13.3 0.000
D. Hemoglobin and soluble transferrin receptor as continuous variables
sTfR (mg/L) 0.0 −0.2, 0.1 0.675
Hb (g/dL) 0.2 0.0, 0.4 0.029
Age (years) −0.2 −0.2, −0.2 0.000
Gender (man) 12.6 12.0, 13.2 0.000
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Note: Results of mixed‐effects linear models with individuals nested within households.

TABLE 4.

Iron deficiency and hemoglobin as predictors of handgrip strength.

Predictor Coefficient 95% Confidence interval p
Iron deficiency (sTfR ≥ 8.3 mg/L) −1.6 −3.0, −0.2 0.021
Hemoglobin (continuous, in g/dL) 0.2 0.0, 0.4 0.076
Age (years) −0.2 −0.2, −0.2 0.000
Gender (man) 12.7 12.0, 13.3 0.000
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Note: Results of mixed‐effects linear models with individuals nested within households.

Inclusion of BMI category attenuated the association between Hb and HGS. Gender interacted with BMI category, but not iron deficiency or anemia. In our final model (Table 5; Figure 1), a clear negative association between iron deficiency and HGS was apparent (Coef: −1.7; 95% CI: −2.9, −0.4). In addition, HGS was lower among underweight men (Coef: −3.1; 95% CI: −4.3, −2.0) and higher among overweight (Coef: 2.2; 95% CI: 0.9, 3.6) and obese men (Coef: 1.9; 95% CI: 0.7, 3.0) but was unassociated with BMI among underweight women (Coef: −0.4; 95% CI: −1.6, 0.7), overweight women (Coef: 0.4; 95% CI: −0.6, 1.4), or obese women (Coef: 0.2; 95% CI: −0.6, 0.9; coefficients for each interaction were calculated from the model in Table 5). Body fat % was unassociated with grip strength (Coef: −0.02; 95% CI: −0.08, 0.03; Table S1).

TABLE 5.

Final model of hand grip strength.

Predictor Coefficient 95% Confidence interval p
Iron deficiency (sTfR ≥ 8.3 mg/L) −1.7 −3.1, −0.4 0.010
Hemoglobin (continuous, in g/dL) 0.1 −0.1, 0.3 0.306
BMI category
Underweight −0.5 −1.7, 0.7 0.390
Lean Ref
Overweight 0.3 −0.7, 1.3 0.601
Obese 0.1 −0.7, 0.9 0.809
Gender (man) 12.7 11.8, 13.6 0.000
BMI × gender
Underweight × man −2.7 −4.3, −1.1 0.001
Lean × man Ref
Overweight × man 2.0 0.3, 3.7 0.018
Obese × man 1.8 0.4, 3.2 0.013
Age −0.2 −0.2, −0.2 0.000
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Note: Results of mixed‐effects linear models with individuals nested within households.

FIGURE 1.

FIGURE 1

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Body mass index was associated with grip strength among men (red) but not women (blue) in a random sample of adults in rural Matlab, Bangladesh, controlling for iron deficiency, hemoglobin, and age.

The interaction between BMI and gender in Table 5 may result from sex differences in body composition, with a larger contribution of adiposity to BMI among females and muscle mass among males. To assess this, we tested for an interaction between whole BF% and gender in predicting BMI; this model suggests a stronger association between BF% and BMI among women than men (p for interaction: 0.065; Coef for women: 0.44; 95% CI: 0.42, 0.46; Coef for men: 0.41; 95% CI: 0.37, 0.44; Table S2).

The interaction between BMI and gender in Table 2 may also be driven by greater age or frailty among men than women, due in part to the migration of working age, healthy men out of Matlab (and thus, out of our sample). To begin to assess this, we tested for gender divergent associations between age and HGS. This model suggests strong evidence for a steeper negative association among men than women (p for interaction: 0.000; Table S3 and Figure S2).

4. Discussion

We expected both iron deficiency and anemia to adversely affect muscle strength among adults in our population‐based sample in rural Bangladesh. As expected, we found a negative association between iron deficiency and HGS, but surprisingly, we found no association between anemia and HGS once the effect of iron deficiency was controlled. Hemoglobin as a continuous variable was similarly unimportant in understanding HGS once iron deficiency and BMI category were included in models. These results point to the importance of iron nutrition in muscle strength and work capacity (Camaschella 2019; Srai et al. 2002; Stugiewicz et al. 2016) and suggest that iron deficient and non‐iron deficient anemias may have differential impacts on muscle function.

Most participants with anemia in this sample were not iron deficient (by our more conservative sTfR definition), suggesting that anemia in this setting may have another cause. Non‐iron deficiency anemia can arise from a wide range of causes, including blood loss in the gut due to parasitic infection (Banu et al. 2014; Hyder et al. 2001), other micronutrient deficiency (Ahmed et al. 2016), menstrual bleeding (Turner et al. 2025), and betel quid use (Sznajder et al. 2023). Most existing studies of HGS and anemia have not directly addressed the role of iron deficiency; our findings suggest these investigations are incomplete. Future research should assess both iron deficiency and anemia to understand how these two related dimensions of nutrition impact muscle function, ability to do work, and ultimately, well‐being.

Among men in our sample, but not women, underweight BMI seemed to compromise HGS, while overweight or obese BMI seemed to augment it. This gender difference could reflect sex differences in body composition, potentially exacerbated by gendered patterns in physical activity in Matlab: men's BMIs in this setting are less strongly influenced by adiposity and may be more strongly influenced by muscle mass. In addition, greater frailty among adult men (resulting in part from higher rates of migration out of Matlab by healthy men; Alam et al. 2024), as evidenced by steeper declines in HGS with age among men, may contribute to gender differences in the association between BMI and grip strength. It seems likely that both differences in body composition and the older age and higher frailty of men in our sample contribute to the gender differences we observed in associations between BMI and HGS.

Muscle strength and work capacity are critical for subsistence and other economic activities, as well as household work, including caring for children. They also influence mobility and thus social engagement. These impacts extend well beyond the individual, to other members of households and communities. Adults' ability to engage in productive activities, social activities, and parenting is critical for their own physical and mental health and well‐being, as well as those of their family members, and their broader engagement in their community. Thus, our results suggest that iron deficiency (whether or not anemia is present) and underweight, by impairing muscle strength and work capacity, may have far‐reaching consequences.

4.1. Limitations

While women were randomly sampled, men were included only if they were married to a participant in the original sampling wave and were present at the time of data collection. This sampling strategy may have introduced bias through the exclusion of labor migrants and unmarried men; however, it is unusual for adult men in Matlab to remain unmarried, and exclusion of labor migrants allows our sample to reflect the real composition of the study population, as most labor migrants spend the majority of their time outside of Matlab (primarily in urban centers in Bangladesh or abroad), with some being absent for years at a time. One of the weaknesses of sTfR as a biomarker of iron deficiency is the lack of a consensus definition for iron deficiency. We trialed two cutpoints drawn from the literature and found a dramatic difference in the rate at which they characterized participants as iron deficient; only the higher cutpoint supported an association between iron deficiency and HGS. This definition may have misclassified or under‐ascertained iron deficiency among participants. These analyses are cross‐sectional, and thus limited in their ability to support causal inference, although we believe it more likely that nutrition impacts muscle strength than the reverse.

5. Conclusion

Iron deficiency (among men and women) and underweight (among men) impact muscle strength and capacity for work, with likely implications for well‐being at the individual, household, and community levels.

Author Contributions

M.K.S., S.C., and T.B. obtained funding. M.K.S., N.A., and K.W. designed the study. M.K.S., N.A., and R.R. trained field staff and oversaw data collection. K.W., R.R., F.H., and A.K. performed laboratory analyses. K.W. performed statistical analyses. M.K.S., K.W., V.D., E.G., J.L., G.N., L.S., and J.S. contributed to theoretical framing. V.D., E.G., J.L., G.N., L.S., J.S., and K.W. drafted the paper; all authors edited the paper.

Funding

The US National Science Foundation (BCS‐1461522, BCS‐1461514, and BCS‐1839269) and The Pennsylvania State University funded this research.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: The distribution of handgrip strength by gender.

Table S1: Final model of grip strength including whole body fat % (BF%).

Table S2: Body fat percent and gender as predictors of body mass index.

Table S3: Interaction of age and sex in predicting grip strength.

Figure S2: Age was more strongly associated with grip strength among men (red) than women (blue) in a random sample of adults in rural Matlab, Bangladesh, controlling for iron deficiency, hemoglobin, and body mass index.

AJHB-38-e70238-s001.pdf (144.3KB, pdf)

Acknowledgments

The authors would like to thank our study participants, and acknowledge Fayeza Sultana, Sufia Sultana, Tanima Rashid, Shamsun Nahar, Lutfa Begum, Ummehani Akter, Fatema Khatun, Borhan Uddin, and Reyad Hassan for invaluable assistance with data collection.

Data Availability Statement

Data are available upon request to the last author, subject to ethics restrictions on identifiable human subjects' data.

References

  1. Ahmed, F. , Prendiville N., and Narayan A.. 2016. “Micronutrient Deficiencies Among Children and Women in Bangladesh: Progress and Challenges.” Journal of Nutritional Science 5: e46. 10.1017/jns.2016.39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Alam, N. , Haider M. M., Rahman M. M., Bashar M. I., Amin M. T., and Wander K.. 2024. “Do Healthy People Migrate More? A 21‐Year Follow‐Up of a Rural Cohort in Bangladesh.” Heliyon 10, no. 20. 10.1016/j.heliyon.2024.e39647. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Aydik, M. , and Ertuğrul B.. 2023. “An Anthropological Perspective on Hand Grip Strength as a Marker of Health, Disease and Fitness.” Hitit Journal of Social Sciences 16, no. 1: 95–113. [Google Scholar]
  4. Bailey, K. V. , and Ferro‐Luzzi A.. 1995. “Use of Body Mass Index of Adults in Assessing Individual and Community Nutritional Status.” Bulletin of the World Health Organization 73, no. 5: 673. [PMC free article] [PubMed] [Google Scholar]
  5. Banu, H. , Khanum H., and Hossain M. A.. 2014. “Relationships Between Anaemia and Parasitic Infections in Adolescent Girls of Bangladesh.” Bangladesh Journal of Zoology 42, no. 1: 1. 10.3329/bjz.v42i1.23340. [DOI] [Google Scholar]
  6. Bohannon, R. W. 2019. “Grip Strength: An Indispensable Biomarker for Older Adults.” Clinical Interventions in Aging 14: 1681–1691. 10.2147/CIA.S194543. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bohannon, R. W. , Maljanian R., and Ferullo J.. 2004. “Mortality and Readmission of the Elderly One Year After Hospitalization for Pneumonia.” Aging Clinical and Experimental Research 16, no. 1: 22–25. 10.1007/BF03324527. [DOI] [PubMed] [Google Scholar]
  8. Camaschella, C. 2019. “Iron Deficiency.” Blood 133, no. 1: 30–39. 10.1182/blood.2022018610. [DOI] [PubMed] [Google Scholar]
  9. Chaves, P. H. M. 2008. “Functional Outcomes of Anemia in Older Adults.” Seminars in Hematology 45, no. 4: 255–260. 10.1053/j.seminhematol.2008.06.005. [DOI] [PubMed] [Google Scholar]
  10. Davies, K. J. , Maguire J. J., Brooks G. A., Dallman P. R., and Packer L.. 1982. “Muscle Mitochondrial Bioenergetics, Oxygen Supply, and Work Capacity During Dietary Iron Deficiency and Repletion.” American Journal of Physiology. Endocrinology and Metabolism 242, no. 6: E418–E427. 10.1152/ajpendo.1982.242.6.E418. [DOI] [PubMed] [Google Scholar]
  11. Davies, S. , Henthorn J. S., Win A. A., and Brozovic M.. 1984. “Effect of Blood Transfusion on Iron Status in Sickle Cell Anaemia.” Clinical and Laboratory Haematology 6, no. 1: 17–22. 10.1111/j.1365-2257.1984.tb00521.x. [DOI] [PubMed] [Google Scholar]
  12. de Araújo, A. C. , Amaral T. L., Monteiro G. T., de Vasconcellos M. T., and Portela M. C.. 2020. “Factors Associated With Low Handgrip Strength in Older People: Data of the Study of Chronic Diseases (Edoc‐I).” BMC Public Health 20, no. 1: 395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. El Shemy, S. A. , Amer F. E., and Madani H. A.. 2019. “Impact of Iron Deficiency Anemia on Functional Abilities and Muscle Strength in Children With Spastic Cerebral Palsy.” Pakistan Journal of Biological Sciences: PJBS 22, no. 5: 214–219. [DOI] [PubMed] [Google Scholar]
  14. Frosch, A. E. , Ayodo G., Odhiambo E. O., Ireland K., Vulule J., and Cusick S. E.. 2018. “Iron Deficiency Is Prevalent Among HIV‐Infected Kenyan Adults and Is Better Measured by Soluble Transferrin Receptor Than Ferritin.” http://ir.jooust.ac.ke/handle/123456789/9418. [DOI] [PMC free article] [PubMed]
  15. Fukushima, T. , Nakano J., Ishii S., et al. 2019. “Influence of Hemoglobin Level on Muscle and Physical Functions, Activities of Daily Living, and Quality of Life in Patients With Hematological Malignancies.” Integrative Cancer Therapies: 1534735419842196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gi, Y. , Jung B., Kim K.‐W., Cho J.‐H., and Ha I.‐H.. 2020. “Low Handgrip Strength Is Closely Associated With Anemia Among Adults: A Cross‐Sectional Study Using Korea National Health and Nutrition Examination Survey (KNHANES).” PLoS One 15, no. 3: e0218058. 10.1371/journal.pone.0218058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Haas, J. D. , and Brownlie T.. 2001. “Iron Deficiency and Reduced Work Capacity: A Critical Review of the Research to Determine a Causal Relationship.” Journal of Nutrition 131, no. 2: 676S–690S. 10.1093/jn/131.2.676S. [DOI] [PubMed] [Google Scholar]
  18. Hall, D. M. B. , and Cole T. J.. 2006. “What Use Is the BMI?” Archives of Disease in Childhood 91, no. 4: 283. 10.1136/adc.2005.077339. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Haslam, A. , Hausman D. B., Davey A., Cress M. E., Johnson M. A., and Poon L. W.. 2012. “Associations of Anemia and Physical Function in Georgia Centenarians.” Journal of the American Geriatrics Society 60, no. 12: 2362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hewawasam, E. , Liu G., Jeffery D. W., Gibson R. A., and Muhlhausler B. S.. 2018. “Estimation of the Volume of Blood in a Small Disc Punched From a Dried Blood Spot Card.” European Journal of Lipid Science and Technology 120, no. 3: 1700362. 10.1002/ejlt.201700362. [DOI] [Google Scholar]
  21. Hirani, V. , Naganathan V., Blyth F., et al. 2016. “Low Hemoglobin Concentrations Are Associated With Sarcopenia, Physical Performance, and Disability in Older Australian Men in Cross‐Sectional and Longitudinal Analysis: The Concord Health and Ageing in Men Project.” Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 71, no. 12: 1667–1675. [DOI] [PubMed] [Google Scholar]
  22. Hurrell, R. , and Egli I.. 2010. “Iron Bioavailability and Dietary Reference Values.” American Journal of Clinical Nutrition 91, no. 5: 1461S–1467S. 10.3945/ajcn.2010.28674F. [DOI] [PubMed] [Google Scholar]
  23. Hyder, S. Z. , Persson L.‐Å., Chowdhury A. M. R., and Ekström E.‐C.. 2001. “Anaemia Among Non‐Pregnant Women in Rural Bangladesh.” Public Health Nutrition 4, no. 1: 79–83. 10.1079/PHN200055. [DOI] [PubMed] [Google Scholar]
  24. Joosten, E. , Detroyer E., and Milisen K.. 2016. “Effect of Anaemia on Hand Grip Strength, Walking Speed, Functionality and 1 Year Mortality in Older Hospitalized Patients.” BMC Geriatrics 16, no. 1: 153. 10.1186/s12877-016-0326-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lee, B. J. , and Chi J. H.. 2023. “Association Between Anemia and Grip Strength Indices Combined With Anthropometry in the Korean Population.” Scientific Reports 13, no. 1: 18517. 10.1038/s41598-023-45985-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Leong, D. P. , Teo K. K., Rangarajan S., et al. 2015. “Prognostic Value of Grip Strength: Findings From the Prospective Urban Rural Epidemiology (PURE) Study.” Lancet 386, no. 9990: 266–273. 10.1016/S0140-6736(14)62000-6. [DOI] [PubMed] [Google Scholar]
  27. Mainous, A. G. , Tanner R. J., Anton S. D., and Jo A.. 2015. “Grip Strength as a Marker of Hypertension and Diabetes in Healthy Weight Adults.” American Journal of Preventive Medicine 49, no. 6: 850–858. 10.1016/j.amepre.2015.05.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Marzban, M. , Nabipour I., Farhadi A., et al. 2021. “Association Between Anemia, Physical Performance and Cognitive Function in Iranian Elderly People: Evidence From Bushehr Elderly Health (BEH) Program.” BMC Geriatrics 21, no. 1: 329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. McDade, T. , and Shell‐Duncan B.. 2004. “Use of Combined Measures From Capillary Blood to Assess Iron Deficiency in Rural Kenyan Children.” Journal of Nutrition 134, no. 2: 384–387. 10.1093/jn/134.2.384. [DOI] [PubMed] [Google Scholar]
  30. Mei, J. V. , Alexander J. R., Adam B. W., and Hannon W. H.. 2001. “Use of Filter Paper for the Collection and Analysis of Human Whole Blood Specimens.” Journal of Nutrition 131, no. 5: 1631S–1636S. 10.1093/jn/131.5.1631S. [DOI] [PubMed] [Google Scholar]
  31. Norman, K. , Stobäus N., Gonzalez M. C., Schulzke J.‐D., and Pirlich M.. 2011. “Hand Grip Strength: Outcome Predictor and Marker of Nutritional Status.” Clinical Nutrition 30, no. 2: 135–142. 10.1016/j.clnu.2010.09.010. [DOI] [PubMed] [Google Scholar]
  32. Penninx, B. W. J. H. , Pahor M., Cesari M., et al. 2004. “Anemia Is Associated With Disability and Decreased Physical Performance and Muscle Strength in the Elderly.” Journal of the American Geriatrics Society 52, no. 5: 719–724. [DOI] [PubMed] [Google Scholar]
  33. Phiri, K. S. , Calis J. C. J., Siyasiya A., Bates I., Brabin B., and van Hensbroek M. B.. 2009. “New Cut‐Off Values for Ferritin and Soluble Transferrin Receptor for the Assessment of Iron Deficiency in Children in a High Infection Pressure Area.” Journal of Clinical Pathology 62, no. 12: 1103–1106. 10.1136/jcp.2009.066498. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Qin, Y. , Melse‐Boonstra A., Pan X., et al. 2013. “Anemia in Relation to Body Mass Index and Waist Circumference Among Chinese Women.” Nutrition Journal 12, no. 1: 10. 10.1186/1475-2891-12-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Rantanen, T. , Harris T., Leveille S. G., et al. 2000. “Muscle Strength and Body Mass Index as Long‐Term Predictors of Mortality in Initially Healthy Men.” Journals of Gerontology, Series A: Biological Sciences and Medical Sciences 55, no. 3: M168–M173. 10.1093/gerona/55.3.M168. [DOI] [PubMed] [Google Scholar]
  36. Russell, M. K. 2015. “Functional Assessment of Nutrition Status.” Nutrition in Clinical Practice 30, no. 2: 211–218. 10.1177/0884533615570094. [DOI] [PubMed] [Google Scholar]
  37. Saad, R. A. , and Qutob H. M.. 2022. “The Relationship Between Anemia and Obesity.” Expert Review of Hematology 15, no. 10: 911–926. 10.1080/17474086.2022.2131521. [DOI] [PubMed] [Google Scholar]
  38. Santos, P. H. , Carmo É. A., Carneiro J. A., Nery A. A., and Casotti C. A.. 2019. “Handgrip Strength: An Effective Screening Instrument for Anemia in the Elderly Women.” Public Health Nursing 36, no. 2: 178–183. [DOI] [PubMed] [Google Scholar]
  39. Selvi Öztorun, H. , Çınar E., Turgut T., et al. 2018. “The Impact of Treatment for Iron Deficiency and Iron Deficiency Anemia on Nutritional Status, Physical Performance, and Cognitive Function in Geriatric Patients.” European Geriatric Medicine 9, no. 4: 493–500. 10.1007/s41999-018-0065-z. [DOI] [PubMed] [Google Scholar]
  40. Siddiquee, T. , Bhowmik B., Da Vale Moreira N. C., et al. 2015. “Prevalence of Obesity in a Rural Asian Indian (Bangladeshi) Population and Its Determinants.” BMC Public Health 15, no. 1: 860. 10.1186/s12889-015-2193-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Srai, S. K. S. , Bomford A., and McArdle H. J.. 2002. “Iron Transport Across Cell Membranes: Molecular Understanding of Duodenal and Placental Iron Uptake.” Best Practice & Research Clinical Haematology 15, no. 2: 243–259. 10.1053/beha.2002.0003. [DOI] [PubMed] [Google Scholar]
  42. Stugiewicz, M. , Tkaczyszyn M., Kasztura M., Banasiak W., Ponikowski P., and Jankowska E. A.. 2016. “The Influence of Iron Deficiency on the Functioning of Skeletal Muscles: Experimental Evidence and Clinical Implications.” European Journal of Heart Failure 18, no. 7: 762–773. 10.1002/ejhf.467. [DOI] [PubMed] [Google Scholar]
  43. Sutandyo, N. , Rinaldi I., Sari N., and Winston K.. 2022. “Prevalence of Anemia and Factors Associated With Handgrip Strength in Indonesia Elderly Population.” Cureus 14, no. 5: e25290. 10.7759/cureus.25290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sznajder, K. K. , Shenk M. K., Alam N., et al. 2023. “Betel Quid Use Is Associated With Anemia Among Both Men and Women in Matlab, Bangladesh.” PLOS Global Public Health 3, no. 6: e0001677. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Thein, M. , Ershler W. B., Artz A. S., et al. 2009. “Diminished Quality of Life and Physical Function in Community‐Dwelling Elderly With Anemia.” Medicine 88, no. 2: 107–114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Turner, J. , Parsi M., and Badireddy M.. 2025. “Anemia.” In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK499994/. [PubMed] [Google Scholar]
  47. Wander, K. , Shell‐Duncan B., and McDade T.. 2009. “Evaluation of Iron Deficiency as a Nutritional Adaptation to Infectious Disease: An Evolutionary Medicine Perspective.” American Journal of Human Biology 21, no. 2: 172–179. 10.1002/ajhb.20839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wang, L. , and Hui S. S.. 2015. “Validity of Four Commercial Bioelectrical Impedance Scales in Measuring Body Fat Among Chinese Children and Adolescents.” BioMed Research International 2015, no. 1: 614858. 10.1155/2015/614858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Warner, M. J. , and Kamran M. T.. 2024. “Iron Deficiency Anemia.” In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK448065/. [PubMed] [Google Scholar]
  50. WHO . 2011. Haemoglobin Concentrations for the Diagnosis of Anaemia and Assessment of Severity. Vitamin and Mineral Nutrition Information System. World Health Organization. https://www.who.int/publications/i/item/WHO‐NMH‐NHD‐MNM‐11.1. [Google Scholar]
  51. Yamada, E. , Takeuchi M., Kurata M., Tsuboi A., Kazumi T., and Fukuo K.. 2015. “Low Haemoglobin Levels Contribute to Low Grip Strength Independent of Low‐Grade Inflammation in Japanese Elderly Women.” Asia Pacific Journal of Clinical Nutrition 24, no. 3: 444–451. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Figure S1: The distribution of handgrip strength by gender.

Table S1: Final model of grip strength including whole body fat % (BF%).

Table S2: Body fat percent and gender as predictors of body mass index.

Table S3: Interaction of age and sex in predicting grip strength.

Figure S2: Age was more strongly associated with grip strength among men (red) than women (blue) in a random sample of adults in rural Matlab, Bangladesh, controlling for iron deficiency, hemoglobin, and body mass index.

AJHB-38-e70238-s001.pdf (144.3KB, pdf)

Data Availability Statement

Data are available upon request to the last author, subject to ethics restrictions on identifiable human subjects' data.

Articles from American Journal of Human Biology are provided here courtesy of Wiley

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