Study protocol: associations between hormonal profile and physical and cognitive functions in middle-aged men—a one-year cohort follow-up study

Background: Middle age (45–64 years) is a critical yet under-researched phase of aging, during which the first signs of physical, cognitive, and hormonal decline often emerge. In men, testosterone (T) levels begin to decrease starting in the fourth decade of life. However, the relationship between T levels and functional abilities in midlife men remains poorly understood. A better understanding of how physical, cognitive, and hormonal domains interact during this transitional phase is essential for developing targeted interventions to promote healthy aging. Using advanced assessment tools, we propose a study aimed to clarify this association in middle-aged men.

Methods: This is a one-year follow-up cohort study. Two hundred healthy middle-aged men will undergo comprehensive assessments, including blood tests, body composition analysis, balance and strength testing, and computerized cognitive evaluation. Half of the sample will perform a subset of the balance and strength tests with a smartphone on their back to obtain accelerometry based measures that have been shown to detect subtle functional changes in midlife. T levels will be measured again after 6 months, and a full assessment will be reperformed after 1 year.

Expected results: We anticipate that men with higher T levels will have better physical and cognitive function, with these associations persisting over a one-year period.

Conclusion: This study addresses a critical research gap by clarifying the relationships between hormonal, physical, and cognitive function during midlife. If significant associations are identified, T level assessments could be integrated into routine preclinical screenings alongside physical and cognitive evaluations.

1 Introduction

Middle age, also known as midlife, is usually defined as the phase of life between the ages of 45 and 64. This period plays a central role in life and is described as the key phase between adulthood and the later stages of life (1). In midlife, human performance can vary greatly from person to person, as physical and cognitive functions begin to decline (2). Up to 20% of the middle-aged population report mobility difficulties, despite having only mild health problems (3). While cognitive abilities such as accumulated knowledge and vocabulary, are often preserved in midlife, complex problem-solving, processing speed and memory may decline (1). These age-related changes can affect independence and quality of life, and increase the risk for unhealthy aging. However, most research on the effects of aging on physical and cognitive function has focused on the older population (4, 5). A better and deeper understanding of the early stages of aging could help to identify individuals who are at higher risk of accelerated decline and allow for the implementation of early interventions to promote healthier aging.

To better understand the mechanisms of aging, it is important to consider the different age-related trajectories of men and women. Women tend to live longer but are more prone to frailty and disability, while men are more likely to be affected by cardiovascular disease and cognitive decline despite maintaining better physical function in midlife (6). These sex-specific differences, which are shaped by biological and hormonal factors, underscore the importance of sex-specific screening tools. Furthermore, while midlife women experience a well-defined hormonal transition with menopause whose health effects have been widely studied, hormonal changes in men, particularly the gradual decline in testosterone (T), are more variable, the presence of associated symptoms does not always correspond to T levels, and have been far less researched (7). Studies indicate that approximately 40% of men experience clinically significant T decline in midlife (8). However, the definition of this period, often referred to as ‘andropause’, remains controversial (9), possibly due to the limited understanding of hormonal changes in men and their relationship to functional abilities.

Low T levels are associated with increased fat mass, decreased muscle mass and metabolic disorders, which can start a feedback loop that further suppresses T levels (10). However, the relationship with physical performance is inconsistent (11). Most studies that have examined this relationship have focused on older men and assessed function using tests such as the sit-to-stand test (STS), tandem stance and walking speed, for which there are no established normative values for middle age. Furthermore, these tests are often not challenging enough and have ceiling effects, so subtle changes in this population may not be detected (12). Recent studies using advanced and more sensitive performance testing tools such as smartphone-based accelerometry in the STS, TUG and Single Leg Stance (SLS) tests have shown that subtle preclinical functional changes can be detected as early as midlife (13, 14). Nevertheless, further research is needed to test these tools in larger samples and in a longitudinal study.

Studies examining the relationship between T levels and cognitive function in adult men have found that higher T levels are linked to a reduced risk of cognitive decline, dementia, and Alzheimer’s disease (15). Evidence suggests that T may protect brain function by slowing glucose metabolism decline and limiting beta-amyloid plaque accumulation associated with Alzheimer’s disease (15). Additionally, several studies have found a positive correlation between T levels and cognitive domains such as memory, attention, and executive function (11, 16). However, most of this research has focused on older adults, underscoring the need to investigate these associations in midlife to better understand the early stages of aging.

Beyond T, hormones such as sex hormone−binding globulin (SHBG), dehydroepiandrosterone sulfate (DHEA-S) and estradiol have also been studied for their role in age-related changes in physical and cognitive function. SHBG levels rise with age and are inversely associated with strength and cognitive function, though these associations often weaken after adjusting for confounders like age (11, 17, 18). DHEA-S correlates positively with executive function and memory in older men, but these associations have not been maintained in longitudinal models (19). DHEA-S is also positively associated with metrics like grip strength and gait speed, particularly in middle-aged men (20). Although there is evidence of an association between estradiol and body composition (21), this does not consistently translate to physical performance (21), and findings on association with cognitive function remain mixed (17, 22).

Modifiable lifestyle and psychological factors such as smoking, sleep quality, depression and anxiety, have also been associated with T levels, cognitive function, and physical performance (23–26). Addressing these factors in midlife research may help to better characterize the early signs of decline and support the development of timely, personalized interventions.

A deeper understanding of the interactions between physical, cognitive and hormonal domains during this transitional phase is essential for the development of personalized interventions to promote healthy aging. Therefore, the primary aims of this study are: (1) To examine associations between physical performance, cognitive function, T levels and self-reported androgen deficiency symptoms in middle-aged men and to assess the dynamics of these associations over 1 year. (2) To characterize physical and cognitive performance of middle-aged men and establish reference values for functional assessments. (3) To examine whether smartphone-based accelerometry correlates more strongly with T levels than other functional tests in middle-aged men. We hypothesize that men with higher T-levels will have better physical performance and cognitive function, that these associations will be maintained over a one-year follow-up period, and that the variables measured with smartphone-based accelerometry will show a stronger correlation with T-levels.

2 Methods

2.1 Study design and procedures

This is a one-year cohort follow-up study, which was approved by the Institutional Review Board of Sheba Medical Center (SMC-0761-23) and complies with the principles outlined in the Declaration of Helsinki.

Participants will be examined three times: at baseline, after 6 months and after 1 year. At baseline and at the one-year follow-up, they will undergo a comprehensive assessment including self-reported background questionnaires, blood sampling, physical and cognitive tests and body composition measurement. At the six-month follow-up, blood samples will be taken, and self-report questionnaires will be completed to monitor short-term hormonal and subjective health changes. A description of the assessments that will be performed at each of the three study time points is presented in Figure 1.

Figure 1

Figure 1. Description of the assessments to be performed at the three study time points (baseline, 6 months, and 12 months). TT, Total testosterone; FT, free testosterone; cBioT, Calculated bioavailable testosterone; SHBG, Sex hormone−binding globulin; DXA, Dual-energy X-ray Absorptiometry; SRT, Sitting-rising test; STS, Sit-to-stand; brief-BESTest, brief-balance evaluation systems test; NeuroTrax- computerized cognitive tests; TUG-DT, Timed up and go with dual task; PAQ, Physical activity Questionnaire.

2.2 Participants

Recruitment will be conducted via center staff, social media, referrals from general practitioners or collaborators, and direct sign-up through the center’s website. Healthy community-dwelling men aged 50–65, able to walk outdoors without assistance, will be included. Participants presenting significant neurological, orthopaedic, cognitive, or visual impairments (e.g., age-related macular degeneration, glaucoma, diabetic retinopathy), psychiatric diagnoses or use of psychiatric medications, or chronic use of medications or supplements known to directly affect T levels (e.g., opioids, T supplements, androgen synthesis inhibitors, anabolic steroids), will be excluded.

The required sample size was calculated using G*Power software (version 3.1.9.4) for a multiple linear regression model with six predictors. Assuming a medium effect size (f² = 0.15), an alpha level of 0.05, and a statistical power of 0.80, a minimum of 98 participants is required to detect a statistically significant association. To increase the reliability of the findings and to account for potential exclusions, data loss, and dropouts during the one-year follow-up, a total of 200 participants will be recruited. Physical performance, cognitive function and T levels will be assessed in all 200 participants. Half of the participants (n = 100) will perform a subset of the balance and strength tests with a smartphone worn on their back to obtain accelerometry based measures (i.e., instrumented physical assessments).

2.3 Data collection and outcome measures

2.3.1 Hormone levels and symptoms

Albumin, SHBG, TT and FT levels will be measured from blood samples collected by a nurse in the early morning after a 12-h fast. The bioavailable T levels will be calculated (cBioT) from the known values of TT, SHBG, and albumin using a simple well-accepted formula (available from: http://www.issam.ch/freetesto.htm).

Androgen deficiency symptoms will be evaluated using the Androgen Deficiency in the Aging Male (ADAM) questionnaire and its quantitative version (qADAM). The ADAM and qADAM questionnaires include 10 binary and scaled items, respectively, and demonstrate high sensitivity (83.3–97%) but low specificity (19.7–60%) (27).

2.3.2 Physical performance

All physical performance assessments will be conducted by experienced physical therapists.

Grip strength will be measured using the Exacta™ Hydraulic Hand Dynamometer (North Coast Medical, Inc., Morgan Hill, CA, USA). Participants will complete three maximal-effort trials per hand, alternating to prevent fatigue, with results averaged for each hand.

The sitting-rising test (SRT) will evaluate the ability to sit and rise from the floor with minimal support (score range: 0–10). Each use of support (e.g., hand or knee) deducts one point, and instability deducts half a point. Lower scores have been associated with increased all-cause mortality and reflect reduced musculoskeletal health (28).

The 30-s STS test will assess lower limb muscle strength. Participants will rise from a chair with wrists crossed over the chest and perform as many repetitions as possible within 30 s.

Balance will be assessed using the brief-balance evaluation systems test (brief-BESTest) (29). The test consists six sub-measures as follows: Biomechanical Constraints tested by hip/trunk lateral strength; Stability Limits tested by functional reach forward; Anticipatory Postural Adjustments tested by the ability to maintain single leg stance in each leg; Reactive Postural Responses tested by compensatory lateral stepping response in each side; Sensory-Orientation tested by stance on foam with eyes closed; and Stability in Gait tested by TUG. Two sub-measures (Anticipatory-Postural- Adjustments and Reactive-Postural-Responses) are scored bilaterally in each leg, resulting in eight test items. Each item scored between 0–3 and the maximal total score is 24 (i.e., a higher score indicates better balance performance).

Cognitive-motor interface will be assessed using the TUG dual task (TUG-DT), in which participants will perform the standard TUG test, while simultaneously counting backward in increments of three from a random three-digit number. The difference in total duration from the standard TUG will be calculated and used as an indicator of dual-task cost.

Instrumented physical assessments will be conducted in half of the sample (n = 100) during the STS, SLS, foam stance and TUG tests. A smartphone (Huawei Honor Play model; Android 10) will be attached to the participants’ lumbar spine near the body’s center of mass, and acceleration data will be recorded with the Phyphox application (RWTH Aachen University, Germany) using the same methodology as previously published (13, 14). For the STS, in addition to the number of repetitions measured in the standard test, the following parameters will be extracted using the smartphone: the average duration of rising phase, peak and average vertical velocity, the consistency of movement across repetitions using the dynamic time-warping (DTW) algorithm applied to the velocity profile, and the average lower limb muscle power during the sit to stand phase (14). Power (W) will be calculated using body mass (kg), vertical acceleration (aᵥ + 9.81 m/s²), and vertical velocity (vᵥ) during the rising phase, using the following formula:

$\text{Power}\left[\mathrm{W}\right]=\text{Body mass}\left[\mathrm{kg}\right]·(\mathrm{av}+9.81)[\mathrm{m}/\mathrm{s}2]·\mathrm{vv}[\mathrm{m}/\mathrm{s}]$

For the SLS and foam stance tests, the instrumented outcomes will include postural sway that will be quantified using the 95% confidence ellipse of the COM (COM-95%) trajectory (13). For the TUG, the turning phase sub-phase duration and the average angular velocity around the vertical axis during turning will be extracted (30). All the extracted data from the instrumented assessments will be processed using MATLAB (Mathworks, Inc.; version 9.12), and the vertical acceleration signal will be filtered with a 4th-order Butterworth low-pass filter with a cutoff frequency of 3 Hz.

2.3.3 Cognitive function

Cognitive function will be assessed using the NeuroTrax™ computerized cognitive test battery (NeuroTrax™ Corp., Modiin, Israel), which has demonstrated high reliability and validity in diverse populations (31). The assessment will be administered under the supervision of a psychologist. The selected subtests target key domains relevant to aging: Verbal Memory (immediate and delayed recall of word pairs), Go/No-Go (attention and executive function via response inhibition and accuracy), Stroop (cognitive flexibility through interference control), and Problem-Solving Spatial (spatial reasoning and abstract thinking). Each test yields domain-specific scores and contributes to a global cognitive score, automatically calculated and standardized to an IQ-like scale (mean = 100, SD = 15), providing a comprehensive cognitive profile for each participant.

2.3.4 Co-variants

To examine the association between T levels, physical performance and cognitive function, data on a set of relevant covariates will be collected. These include age, body composition, physical activity level, smoking status, sleep quality and mental health. The selection of these covariates was informed by a directed acyclic graph (DAG), constructed using DAGitty software (32). This approach helps to minimize bias by clarifying the underlying causal structure and identifying appropriate variables for adjustment. The resulting DAG guided the specification of the regression models by illustrating the potential pathways through which the variables may be interrelated, as shown in Figure 2.

Figure 2

Figure 2. Directed Acyclic Graph (DAG) illustrating the hypothesized causal structure linking testosterone (green; exposure) to physical performance and cognitive function (blue; outcomes). The pink nodes represent variables identified in the literature as potentially affecting both exposure and outcomes, such as age, sleep quality and body composition. These variables were included for statistical adjustment to reduce confounding bias. The model was created using DAGitty.

Participant backgrounds will be assessed through a structured questionnaire covering sociodemographic and general health information. This will include items on income, education level, employment status, overall health, medication use, smoking status, alcohol consumption, family medical history, and self-reported health status and history.

Physical activity (PA) will be assessed using the widely used Baeck questionnaire, which covers work-related, sports, and non-sport leisure activities. Each section score is the average of frequency and intensity ratings on a five-point scale (33).

Body composition Weight and height will be measured to calculate BMI. Dual-energy X-ray absorptiometry (DXA; GE Healthcare; Madison, WI, USA) will be performed by a certified technician from the medical center to assess fat mass index (FMI), fat-free mass index (FFMI), and visceral adipose tissue (VAT).

Sleep quality and insomnia severity will be evaluated using the Insomnia Severity Index (ISI) (34). The ISI evaluated insomnia severity using a 5-point Likert scale for several questions (total scores 0–28), with higher scores indicating greater severity. The ISI demonstrates excellent reliability (Cronbach’s α = 0.90–0.91) and validates well compared to sleep diaries and psychological measures (34).

Mental health will be assessed using two widely recognized, reliable and validated tools: the Beck Anxiety Inventory (BAI) and the Beck Depression Inventory (BDI) (35, 36). Both include 21 items, each scored between zero to three. A total score of over 25 on the BAI or over 28 on the BDI indicates severe anxiety or depression (35, 36).

A summary of all the outcome measures in this study can be found in Table 1.

Table 1

Table 1. Summary of outcome measures.

2.4 Data analysis and statistics

The Shapiro–Wilk test will be employed to assess the normality of data distribution. Descriptive statistics will be presented as mean ± standard deviation (SD) or median with interquartile range (IQR) for continuous variables, and as frequencies and percentages for categorical variables. These analyses are intended to support the objective of establishing reference values for functional assessments in middle-aged men.

To address the first primary aim, Pearson or Spearman correlation coefficients will examine the relationship between physical performance, cognitive function and T levels (FT and cBioT) based on the baseline data. Variables showing moderate correlations (r ≥ 0.2) will be included in multiple linear regression models adjusting for the covariates. Additionally, within-subject changes in T levels, physical function, and cognitive performance over the one-year follow-up period will be assessed using paired t-tests or Wilcoxon signed-rank tests, depending on the distribution of the data. To further explore individual variability in these changes, linear regression models will be used to examine whether baseline characteristics (age, BMI, physical activity, sleep quality, and initial scores in each domain) are associated with the magnitude of change in outcomes. This analysis will help to identify baseline factors associated with functional decline or stability and provide insights into early correlates of functional and hormonal changes in midlife.

To examine whether smartphone-based accelerometry correlates more strongly with T levels than other functional tests in middle-aged men, data from the smartphone based accelerometry will be analyzed to compare the strength of the relationship between physical and cognitive function and T levels. The analyses will be performed separately for each test. For the STS test, associations with the number of repetitions will be compared with advanced metrics including the power (W), peak and average VV (rad/s) and movement consistency (DTW). For balance, the brief-BESTest sub-scores for SLS and foam stance (0–3) will be compared with the postural sway as measured by the COM-95% ((m²)²). For gait stability, the total duration of the TUG will be compared with the phase duration (seconds) and the angular velocity during the first turn (rad/s) measured with the smartphone. Pearson or Spearman correlation coefficients will be calculated for each factor-outcome pair. In addition, multiple linear regression models adjusted for age, BMI, physical activity level, and sleep quality will be used to compare standardized beta coefficients. These analyses will be used to determine whether smartphone-based accelerometry correlates more strongly with T levels than other functional tests in middle-aged men.

All statistical analyses will be performed using IBM SPSS Statistics (version 27) and Python (version 3.12), with a significance set at a p-value of <0.05. Effect sizes will be reported alongside p-values to provide a comprehensive understanding of the magnitude of observed associations or intervention effects.

3 Discussion

The main aim of this study is to explore the relationships between hormone profile, physical performance and cognitive function in healthy middle-aged men, over a one-year period. By integrating these domains within a single research framework, we aim to uncover early preclinical patterns that may signal the onset of age-related decline and enhance the understanding of how these systems interact in the early stages of aging.

Midlife is a critical yet under-researched stage in the aging process. While most studies compare younger and older adults (1), recent findings have shown that aging is a nonlinear process, with significant biologic, physical and cognitive changes occurring in the middle age (37, 38). In particular, middle-aged men are even less studied, as the menopausal transition provides a clearer biological marker for research in women. To our knowledge, no previous study has comprehensively examined hormonal, physical, and cognitive function in healthy middle-aged men.

This study offers a novel, integrative framework that aligns with the WHO healthy aging paradigm by operationalizing key areas of intrinsic capacity, defined as “the composite of all the physical and mental capacities that an individual can draw on” (39). Decline in intrinsic capacity is now recognized as a predictor of adverse outcomes such as disability, frailty, falls, hospitalization and mortality, and there is evidence that this decline can begin as early as midlife (40). However, there are few studies that have examined intrinsic capacity in this age group (40). This protocol addresses this gap by providing a comprehensive perspective on the integration between biological markers (e.g., hormone profile) with physical performance, cognitive function and mental health in middle-aged men, while also considering sleep quality to provide a broader view of the dynamics of early aging.

To address this gap, this protocol employs advanced, objective tools, including accelerometry-based sensors for physical function and the computerized NeuroTrax cognitive battery, both of which have demonstrated suitability for detecting early, subtle changes in performance among healthy adults (13, 14, 31). Recent studies highlight the value of instrumental physical assessments in this population. Baranes et al. (13) have shown that the integration of a smartphone-based accelerometery to measure sway during the Brief-BESTest reveals subtle impairments in balance between early and late middle-aged adults that are not captured by the standard assessment method. Consistent with these findings, Hayek et al. (14) demonstrated that smartphone-based STS analysis improves accuracy in detecting early mobility deterioration in middle-aged adults. While conventional timing measures of STS generally fail to detect meaningful differences between young and middle-aged adults, accelerometry-derived measures such as lower-limb power and maximal vertical velocity showed significant functional declines in the middle-aged group (14). The TUG also showed a better ability to detect differences in functional performance when applied in its instrumented form (30). In particular, parameters of the turning phase, such as turning duration and angular velocity, have been shown to be effective in detecting early balance impairments in midlife, as turning is a complex task that reflects age-related declines in postural control and has been associated with age-related decline and increased fall risk (30). The use of smartphone-based sensors, which are found in almost every pocket, could enable the development of accessible, low-cost, and scalable functional assessment tools that can be used in any clinical setting and are potentially suitable for unsupervised home monitoring without the need for an in-person physician visit. Furthermore, this protocol could help establish reference values for physical performance in healthy middle-aged adults, a gap that remains largely unaddressed in the current literature.

This one-year cohort design with three measurement points provides the opportunity to move beyond cross-sectional observations and determine whether the identified associations represent transient variability or stability. Assessing participants over time allows for the identification of enduring functional and cognitive patterns that may serve as meaningful early markers of aging and enable the development of targeted prevention strategies.

While this study protocol offers novel insights into the associations between hormones, physical performance, and cognition in midlife, several limitations should be considered. First, to minimize potential confounding factors, the study will include only healthy, community-dwelling middle-aged men and exclude those with health conditions that could affect physical or cognitive performance. Additionally, the recruitment strategy (e.g., social media, referrals), although chosen for feasibility, may introduce selection bias. Both factors should be considered when interpreting the findings, as they may limit the generalizability of the results. Second, the one-year follow-up permits evaluation of short-term stability of associations, but longer longitudinal studies are required to capture gradual hormonal and functional changes and to determine whether early markers persist or evolve over time. Third, while the focus of this protocol on androgen related-hormones (TT, FT, cBioT, SHBG) is consistent with the study aims and with previous studies in this field (15, 16, 18), future studies should consider including additional hormones (e.g., cortisol, growth hormone) to provide a more comprehensive understanding of the biological mechanisms underlying age-related functional changes in midlife.

In summary, by examining the early interactions between biological, functional, cognitive, and mental systems in midlife, using objective, tailored assessment tools that have proven effective in capturing variability in this population, our study is expected to provide new insights into the onset of aging processes. Focusing on this overlooked life stage and integrating objective assessments across multiple domains will provide the evidence base needed for early detection and support further studies to develop prevention strategies aimed at promoting healthier aging in this population.

Ethics statement

This study, involving humans, was approved by the Institutional Review Board of Sheba Medical Center (SMC-0761-23). The study will be conducted following the principles of the Declaration of Helsinki. This study was conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

DK: Conceptualization, Methodology, Visualization, Writing – original draft. AG: Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing. TS: Resources, Writing – review & editing. SS: Conceptualization, Methodology, Supervision, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Acknowledgments

We would like to thank the clinical team at the Sheba Longevity Center for their support in participant recruitment and data collection, and the team of the Neuromuscular and Human Performance Laboratory at Ariel University for their valuable assistance in implementing and supporting the smartphone sensor-based measurement and analysis framework for this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

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References

1. Lachman, ME. Mind the gap in the middle: a call to study midlife. Res Hum Dev. (2015) 12:327–34. doi: 10.1080/15427609.2015.1068048

PubMed Abstract | Crossref Full Text | Google Scholar

2. Brown, RT, and Covinsky, KE. Moving prevention of functional impairment upstream: is middle age an ideal time for intervention? Womens Midlife Health. (2020) 6:4. doi: 10.1186/s40695-020-00054-z

PubMed Abstract | Crossref Full Text | Google Scholar

3. Gardener, EA, Huppert, FA, Guralnik, JM, and Melzer, D. Middle-aged and mobility-limited prevalence of disability and symptom attributions in a national survey. J Gen Intern Med. (2006) 21:1091–6. doi: 10.1111/j.1525-1497.2006.00564.x

PubMed Abstract | Crossref Full Text | Google Scholar

4. Kim, M, Kwasny, MJ, Bailey, SC, Benavente, JY, Zheng, P, Bonham, M, et al. MidCog study: a prospective, observational cohort study investigating health literacy, self-management skills and cognitive function in middle-aged adults. BMJ Open. (2023) 13:899. doi: 10.1136/bmjopen-2023-071899

Crossref Full Text | Google Scholar

5. Richardson, J, Beauchamp, M, Bean, J, Brach, J, Chaves, PHM, Guralnik, JM, et al. Defining and measuring preclinical mobility limitation: an expert consensus exercise informed by a scoping review. J Gerontol A Biol Sci Med Sci. (2023) 78:1641–50. doi: 10.1093/gerona/glad143

PubMed Abstract | Crossref Full Text | Google Scholar

6. Hägg, S, and Jylhävä, J. Sex differences in biological aging with a focus on human studies. eLife. (2021) 10:e63425. doi: 10.7554/eLife.63425

PubMed Abstract | Crossref Full Text | Google Scholar

7. Huhtaniemi, IT. Andropause - lessons from the European male ageing study. Ann Endocrinol. (2014) 75:128–31. doi: 10.1016/j.ando.2014.03.005

PubMed Abstract | Crossref Full Text | Google Scholar

8. Livingston, M, Kalansooriya, A, Hartland, AJ, Ramachandran, S, and Heald, A. Serum testosterone levels in male hypogonadism: why and when to check-a review. Int J Clin Pract. (2017) 71:e12995. doi: 10.1111/ijcp.12995

PubMed Abstract | Crossref Full Text | Google Scholar

9. Pines, A. Male menopause: is it a real clinical syndrome? Climacteric. (2011) 14:15–7. doi: 10.3109/13697137.2010.507442

PubMed Abstract | Crossref Full Text | Google Scholar

10. Pivonello, R, Menafra, D, Riccio, E, Garifalos, F, Mazzella, M, De Angelis, C, et al. Metabolic disorders and male hypogonadotropic hypogonadism. Front Endocrinol. (2019) 10:345. doi: 10.3389/fendo.2019.00345

PubMed Abstract | Crossref Full Text | Google Scholar

11. Kaufman, J-M, and Lapauw, B. Role of testosterone in cognition and mobility of aging men. Andrology. (2020) 8:1567–79. doi: 10.1111/andr.12872

PubMed Abstract | Crossref Full Text | Google Scholar

12. Bergquist, R, Weber, M, Schwenk, M, Ulseth, S, Helbostad, JL, Vereijken, B, et al. Performance-based clinical tests of balance and muscle strength used in young seniors: a systematic literature review. BMC Geriatr. (2019) 19:9. doi: 10.1186/s12877-018-1011-0

PubMed Abstract | Crossref Full Text | Google Scholar

13. Baranes, G, Hayek, R, Gutman, I, Frenkel-Toledo, S, and Springer, S. Use of the brief-BESTest partially instrumented with accelerometry to detect balance deterioration in middle-age. Aging Clin Exp Res. (2024) 36:214. doi: 10.1007/s40520-024-02868-7

PubMed Abstract | Crossref Full Text | Google Scholar

14. Hayek, R, Gutman, I, Baranes, G, Nudelman, Y, and Springer, S. Smartphone-based sit-to-stand analysis for mobility assessment in middle age. Innov Aging. (2024) 8:igae079. doi: 10.1093/geroni/igae079

PubMed Abstract | Crossref Full Text | Google Scholar

15. Holland, J, Bandelow, S, and Hogervorst, E. Testosterone levels and cognition in elderly men: a review. Maturitas. (2011) 69:322–37. doi: 10.1016/j.maturitas.2011.05.012

PubMed Abstract | Crossref Full Text | Google Scholar

16. Dong, X, Jiang, H, Li, S, and Zhang, D. Low serum testosterone concentrations are associated with poor cognitive performance in older men but not women. Front Aging Neurosci. (2021) 13:712237. doi: 10.3389/fnagi.2021.712237

PubMed Abstract | Crossref Full Text | Google Scholar

17. Yaffe, K, Lui, LY, Zmuda, J, and Cauley, J. Sex hormones and cognitive function in older men. J Am Geriatr Soc. (2002) 50:702–12. doi: 10.1046/j.1532-5415.2002.50166.x

Crossref Full Text | Google Scholar

18. Krasnoff, JB, Basaria, S, Pencina, MJ, Jasuja, GK, Vasan, RS, Ulloor, J, et al. Free testosterone levels are associated with mobility limitation and physical performance in community-dwelling men: the Framingham offspring study. J Clin Endocrinol Metab. (2010) 95:2680. doi: 10.1210/jc.2009-2680

Crossref Full Text | Google Scholar

19. Elpers, A-L, and Steptoe, A. Associations between dehydroepiandrosterone sulphate (DHEAS) and cognitive function in 5,061 older men and women in the English longitudinal study of ageing. Psychoneuroendocrinology. (2020) 117:104702. doi: 10.1016/j.psyneuen.2020.104702

PubMed Abstract | Crossref Full Text | Google Scholar

20. Rendina, DN, Ryff, CD, and Coe, CL. Precipitous dehydroepiandrosterone declines reflect decreased physical vitality and function. J Gerontol A Biol Sci Med Sci. (2017) 72:747–53. doi: 10.1093/gerona/glw135

PubMed Abstract | Crossref Full Text | Google Scholar

21. Auyeung, TW, Lee, JSW, Kwok, T, Leung, J, Ohlsson, C, Vandenput, L, et al. Testosterone but not estradiol level is positively related to muscle strength and physical performance independent of muscle mass: a cross-sectional study in 1489 older men. Eur J Endocrinol. (2011) 164:811–7. doi: 10.1530/EJE-10-0952

PubMed Abstract | Crossref Full Text | Google Scholar

22. Yaffe, K, Barnes, D, Lindquist, K, Cauley, J, Simonsick, EM, Penninx, B, et al. Endogenous sex hormone levels and risk of cognitive decline in an older biracial cohort. Neurobiol Aging. (2007) 28:171–8. doi: 10.1016/j.neurobiolaging.2006.10.004

PubMed Abstract | Crossref Full Text | Google Scholar

23. Zhao, J, Leung, JYY, Lin, SL, and Schooling, MC. Cigarette smoking and testosterone in men and women: a systematic review and meta-analysis of observational studies. Prev Med. (2016) 85:1–10. doi: 10.1016/j.ypmed.2015.12.021

Crossref Full Text | Google Scholar

24. Swan, GE, and Lessov-Schlaggar, CN. The effects of tobacco smoke and nicotine on cognition and the brain. Neuropsychol Rev. (2007) 17:259–73. doi: 10.1007/s11065-007-9035-9

PubMed Abstract | Crossref Full Text | Google Scholar

25. Patel, P, Shiff, B, Kohn, TP, and Ramasamy, R. Impaired sleep is associated with low testosterone in US adult males: results from the National Health and nutrition examination survey. World J Urol. (2019) 37:1449–53. doi: 10.1007/s00345-018-2485-2

PubMed Abstract | Crossref Full Text | Google Scholar

26. Ford, AH, Yeap, BB, Flicker, L, Hankey, GJ, Chubb, SAP, Handelsman, DJ, et al. Prospective longitudinal study of testosterone and incident depression in older men: the health in men study. Psychoneuroendocrinology. (2016) 64:57–65. doi: 10.1016/j.psyneuen.2015.11.012

PubMed Abstract | Crossref Full Text | Google Scholar

27. Bernie, AM, Scovell, JM, and Ramasamy, R. Comparison of questionnaires used for screening and symptom identification in hypogonadal men. Aging Male. (2014) 17:195–8. doi: 10.3109/13685538.2014.963041

PubMed Abstract | Crossref Full Text | Google Scholar

28. Brito, LBBD, Ricardo, DR, Araújo, DSMSD, Ramos, PS, Myers, J, and Araújo, CGSD. Ability to sit and rise from the floor as a predictor of all-cause mortality. Eur J Prev Cardiol. (2014) 21:892–8. doi: 10.1177/2047487312471759

PubMed Abstract | Crossref Full Text | Google Scholar

29. Padgett, PK, Jacobs, JV, and Kasser, SL. Is the Bestest at its best? A suggested brief version based on interrater reliability, validity, internal consistency, and theoretical construct. Phys Ther. (2012) 92:1197–207. doi: 10.2522/ptj.20120056

PubMed Abstract | Crossref Full Text | Google Scholar

30. Ortega-Bastidas, P, Gómez, B, Aqueveque, P, Luarte-Martínez, S, and Cano-de-la-Cuerda, R. Instrumented timed up and go test (iTUG)—more than assessing time to predict falls: a systematic review. Sensors. (2023) 23:3426. doi: 10.3390/s23073426

PubMed Abstract | Crossref Full Text | Google Scholar

31. Dwolatzky, T, Whitehead, V, Doniger, GM, Simon, ES, Schweiger, A, Jaffe, D, et al. Validity of a novel computerized cognitive battery for mild cognitive impairment. BMC Geriatr. (2003) 3:4. doi: 10.1186/1471-2318-3-4

PubMed Abstract | Crossref Full Text | Google Scholar

32. Textor, J, Hardt, J, and Knüppel, S. DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology. (2011) 22:745. doi: 10.1097/EDE.0b013e318225c2be

PubMed Abstract | Crossref Full Text | Google Scholar

33. Baecke, J, Burema, J, and Frijters, J. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr. (1982) 36:936–42. doi: 10.1093/ajcn/36.5.936

PubMed Abstract | Crossref Full Text | Google Scholar

34. Morin, CM, Belleville, G, Bélanger, L, and Ivers, H. The insomnia severity index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. (2011) 34:601–8. doi: 10.1093/sleep/34.5.601

PubMed Abstract | Crossref Full Text | Google Scholar

35. Fydrich, T, Dowdall, D, and Chambless, DL. Reliability and validity of the Beck anxiety inventory. J Anxiety Disord. (1992) 6:55–61. doi: 10.1016/0887-6185(92)90026-4

Crossref Full Text | Google Scholar

36. Richter, P, Werner, J, Heerlein, A, Kraus, A, and Sauer, H. On the validity of the Beck depression inventory. A review. Psychopathology. (1998) 31:160–8. doi: 10.1159/000066239

PubMed Abstract | Crossref Full Text | Google Scholar

37. Antal, BB, Van Nieuwenhuizen, H, Chesebro, AG, Strey, HH, Jones, DT, Clarke, K, et al. Brain aging shows nonlinear transitions, suggesting a midlife “critical window” for metabolic intervention. Proc Natl Acad Sci. (2025) 122:e2416433122. doi: 10.1073/pnas.2416433122

PubMed Abstract | Crossref Full Text | Google Scholar

38. Shen, X, Wang, C, Zhou, X, Zhou, W, Hornburg, D, Wu, S, et al. Nonlinear dynamics of multi-omics profiles during human aging. Nat Aging. (2024) 4:1619–34. doi: 10.1038/s43587-024-00692-2

PubMed Abstract | Crossref Full Text | Google Scholar

39. WHO. World report on ageing and health. Geneva: World Health Organization (2015).

Google Scholar

40. Zhao, Y, Jiang, Y, Tang, P, Wang, X, Guo, Y, and Tang, L. Adverse health effects of declined intrinsic capacity in middle-aged and older adults: a systematic review and meta-analysis. Age Ageing. (2024) 53:afae162. doi: 10.1093/ageing/afae162

PubMed Abstract | Crossref Full Text | Google Scholar