Kazem Khalagi

Introduction: Thyroid hormones regulate key metabolic processes; however, the association between thyroid function and amino acid metabolism varies across studies. This study aimed to investigate the relationship between thyroid biomarkers and circulating amino acid concentrations in adults aged 50 years and older. Methods: We initially enrolled a random subsample of 824 adults aged 50 years and older from the second recruitment of the Bushehr Elderly Health (BEH) Program. Because the clinical and subclinical hyperthyroidism groups were very small, 14 participants from these groups were excluded from the primary between‐group and regression analyses. Therefore, the primary analytical sample comprised 810 participants with euthyroid status, subclinical hypothyroidism, or clinical hypothyroidism. Associations between amino acid concentrations and thyroid biomarkers were assessed using multivariable linear regression models. Results: The initial sample included 335 men and 489 women, with mean ages of 62.27 ± 8.57 and 61.47 ± 7.52 years, respectively. Although several nominal associations were observed between amino acids and TSH in crude or adjusted analyses, none remained statistically significant after false discovery rate (FDR) correction. No statistically significant differences in amino acid concentrations were observed between the thyroid disorder groups after FDR correction. Conclusion: No statistically significant associations between amino acid concentrations and thyroid biomarkers remained after correction for multiple comparisons. Likewise, no significant differences in amino acid concentrations were observed between the examined thyroid disorder groups after FDR correction. Larger longitudinal studies are needed to determine whether the nominal findings observed in the present study are reproducible and biologically meaningful.

The Bushehr Elderly Health (BEH) Program is a population-based prospective cohort study, designed to address the increasing health challenges associated with an aging population. The third phase of the BEH program was conducted to repeat measurements from previous phases of the study, assessing the state of non-communicable diseases (NCDs)—particularly cardiovascular and musculoskeletal diseases and cognitive function—as well as their determinants and consequences among older adults. This article presents the study profile for this phase, detailing the methodology, recruitment process, and baseline participant characteristics. The resulting longitudinal data will be used in future analytical studies. Phase III of the BEH Program was conducted six years after the second phase (stage 2), involving repeated measurements among participants aged 60 and above who were residing in Bushehr city and had participated in the previous phases of the first recruitment of the study. Previous phase participants were invited to take part in this phase. This phase repeated the measurements conducted in the previous phases included comprehensive questionnaires, physical and anthropometric examinations, whole body and bone density scan, diagnostic laboratory tests, and biobanking of biological samples including serum, EDP, stool, and saliva. The successful assessment has been completed for participants invited from the earlier phases of the BEH program, yielding extensive data for future analysis. In this phase of the BEH program, a total of 1,345 participants were enrolled in the study, a response rate of 64.7%. The mean (standard deviation) age was 74.3 (4.6) years and 50.4% of the participants were males. The third phase of the BEH program successfully repeated comprehensive measurements and expanded the biobank to include microbiota samples. These repeated measures will provide valuable longitudinal insights once analyzed, enabling researchers to examine changes in health-related variables over time and better understand the needs of older Iranian adults. Future analyses will address the specific objectives related to cardiovascular, musculoskeletal, and cognitive health.

Although individuals having type 2 diabetes mellitus (T2DM) are generally at increased risk for osteoporosis and fractures, they paradoxically tend to exhibit relatively higher bone mineral density BMD T-scores. The trabecular bone score (TBS) has been shown to better gauge bone health in these patients, frequently revealing deterioration that BMD fails to detect. This study aims to investigate the prevalence and determinants of the “TBS worse than BMD” discordance among T2DM patients aged 50 years and older in Iran. This study was based on the data from the first phase of the second recruitment of the Bushehr Elderly Health (BEH) program. Dual-energy x-ray absorptiometry (DXA, Discovery WI Bone Densitometer; Hologic, Bedford, MA, USA) was used for BMD measurement, and TBS iNsight software (version 2.2; Medimaps Group, Plan-les-Ouates, Switzerland) was used for TBS measurement. “TBS worse than BMD” discordance was defined as partially degraded TBS with normal BMD, or degraded TBS with normal BMD, or degraded TBS with osteopenia. The discordance prevalence was determined, and univariable and multivariable logistic regression analyses were performed to identify the factors associated with the discordance. Our study involved 363 participants with T2DM, of whom 125 (34.44%) were men and 238 (65.56%) were women. The mean age (± SD) was 63.83 (± 7.35) years. The “TBS worse than BMD” discordance was observed in 65 (17.91%, 95% CI 14.28, 22.21) subjects. The discordance was significantly more prevalent in women (21.01%) compared to men (12.00%). After adjusting for other variables, waist circumference emerged as the only significant determinant for the discordance ( P < 0.001). Every 1 cm increase in waist circumference increased the odds of discordance by 13% (OR: 1.13, 95% CI 1.07, 1.19). The “TBS worse than BMD” discordance was present in approximately one out of every five to six older adults with T2DM. Waist circumference emerged as the only significant determinant after adjustment, indicating the potential contribution of central adiposity to this discordance. However, the use of TBS iNsight software (version 2.2) may mean that tissue thickness was not appropriately accounted for in our study. Our findings provide evidence in support of incorporating TBS alongside BMD in fracture risk assessment, particularly among older individuals with T2DM and elevated waist circumference.

Background: Subclinical metabolic disturbances have been associated with functional brain changes; however, the neural pathways mediating their cognitive effects remain unclear. Objectives: This study investigated whether large-scale brain network connectivity mediates associations between metabolic risk factors and cognition in healthy young adults. Methods: Data were obtained from 676 participants (22 - 37 years) in the Human Connectome Project. Metabolic indices (body mass index (BMI), glycated hemoglobin, thyroid-stimulating hormone (TSH), systolic/diastolic blood pressure, and hematocrit) were assessed alongside the National Institutes of Health (NIH) Toolbox Cognition Battery scores, the Mini-Mental State Examination, and fluid intelligence measures. Resting-state fMRI data underwent group independent component analysis to identify 14 intrinsic connectivity networks. Partial correlations were computed between network time series, and multiple regression models were adjusted for age, sex, race, and education. Mediation analyses with bootstrapped confidence intervals were conducted to test whether network connectivity explained the metabolic–cognition relationships. Results: We found that large-scale resting-state networks significantly mediated the associations between specific metabolic risk factors and cognitive performance. Specifically, BMI, hematocrit, and TSH showed significant associations with both cognition and network connectivity (all P < 0.05). BMI-related reductions in vocabulary performance were fully mediated by altered connectivity in the frontoparietal–language, sensorimotor–frontoparietal, primary visual–auditory, and default mode–executive attention networks and partially mediated (compensatory effect) by salience–sensorimotor connectivity (indirect effects: –0.0238, -0.0115, -0.0110, -0.0096, and 0.0068, respectively; 95% CIs: [–0.0421, –0.0085], [-0.0239, -0.0030], [-0.0237, -0.00016], [-0.0206, -0.0014], and [0.0001, 0.0165], respectively). Hematocrit’s positive association with vocabulary performance was partially mediated by the primary visual–auditory, salience–auditory, and language–dorsal attention networks, with primary visual–ventral attention connectivity exerting a suppressive effect (indirect effects: 0.0094, 0.0149, 0.0074, and -0.0113, respectively; 95% CIs: [0.0007, 0.0215], [0.0032, 0.0319], [0.0001, 0.0197], and [-0.0249, -0.0012], respectively). TSH was positively associated with fluid intelligence via a direct pathway (P = 0.010), without mediation by resting-state connectivity. Conclusions: These findings highlight large-scale brain networks as potential intermediate phenotypes that link metabolic health to cognition, suggesting that targeted neuromodulation of vulnerable circuits, combined with metabolic interventions, may offer novel strategies for preserving cognition in at-risk populations.

Background Procollagen type 1 N-terminal propeptide (P1NP) is a vital biomarker for evaluating bone formation, reflecting osteoblast activity and offering meaningful insights into bone health. Elevated P1NP levels may signal heightened bone turnover, as observed in conditions like osteoporosis. Conversely, decreased P1NP levels could serve as a prognostic indicator for fracture risk stratification in the elderly population. Objectives This study aimed to establish P1NP levels to enhance diagnostic and therapeutic methods in evaluating bone health. Methods A systematic literature search were performed across multiple databases until April 15, 2023, focusing on articles that measured serum P1NP levels in healthy individuals aged 50 and older. After removing duplicates, two reviewers evaluated titles and abstracts of potentially relevant studies. Data were extracted on authors, publication year, sample size, laboratory kits used, participants’ fasting status, sex, and serum P1NP levels using a pre-designed data sheet. The risk of bias was assessed with the Newcastle Ottawa checklist, and pooled mean P1NP levels were estimated using Stata software, with statistical heterogeneity analysed through Cochran’s Q test and the I² statistic. Results Out of 11,011 studies identified, 49 met the inclusion criteria after screening and removing 4,227 duplicates. The pooled P1NP levels were 39.6 ng/mL for males and 48.5 ng/mL for females, with a combined level of 46.5 ng/mL. Under fasting conditions, P1NP levels were 40.0 ng/mL for males and 49.7 ng/mL for females. Funnel plot analysis suggested publication bias, but the overall quality of the studies was assessed as generally strong. Conclusion The current study points out the necessity for the determination of sex-specific values for P1NP, highlighting elevated P1NP levels in women to be associated with a higher risk of osteoporosis.

Background Understanding whether commonly available metabolic, demographic, and behavioral factors can explain variability in brain structure may support the development of accessible predictive approaches. This study aims to evaluate the ability of machine learning regression models to predict brain structural measures in young, neurologically healthy adults using clinically accessible risk factors. Methods Data were drawn from the Human Connectome Project Young Adults dataset, including 1082 participants aged 22–37. Predictors comprised metabolic measures (systolic and diastolic blood pressure, hematocrit, body mass index, hemoglobin A1C, thyroid stimulating hormone) and demographic/behavioral variables (age, gender, race, education level, smoking history, alcohol use). Brain structural parameters from MRI included gray matter (GM) and white matter (WM) volumes, surface areas, and cortical thickness. Distance correlation (dcor) guided feature selection, followed by machine learning modeling. Model performance was evaluated using normalized mean absolute error (NMAE), normalized root-mean-squared error (NRMSE), and coefficient of determination (R²). SHAP values were used for feature interpretation. Results XGBoost consistently outperformed other algorithms, showing strong predictive accuracy for total brain segmentation, supratentorial, and total GM volumes (NMAE < 0.35, R² > 0.80). In contrast, cortical surface predictions (left superior frontal) showed moderate performance (NMAE > 0.50, R² < 0.60). SHAP analysis highlighted age, gender, education level, and diastolic and systolic blood pressure as the most influential predictors of brain structure. Conclusions The findings highlight the methodological value of regression-based approaches for estimating brain structural variability and support their potential role as complementary tools for brain health research.

Background: This cross-sectional study investigated novel anthropometric indices including the triglyceride-glucose (TyG) index, visceral adiposity index (VAI), and lipid accumulation product (LAP), which have been proposed as simple markers of insulin resistance. A factor contributing to cognitive impairment through mechanisms such as endothelial dysfunction, inflammation, and neuronal injury. Due to these Insulin resistance-related pathways, such indices may help identify older adults at higher risk of cognitive impairment. This study aimed to investigate the associations between TyG, VAI, and LAP and cognitive function in Iranian older adults using data from the Bushehr Elderly Health (BEH) program. Methods: This cross-sectional analysis used data from 2,426 participants aged ≥60 in the Bushehr Elderly Health program. Cognitive function was assessed using the functional assessment staging test (FAST), Mini-Cog, and category fluency test (CFT) questionnaires. Anthropometric indices (TyG, VAI, LAP) were calculated from clinical and laboratory measurements. Logistic regression, stratified by sex a priori and adjusted for demographic, lifestyle, and clinical confounders selected through backward stepwise regression, was used to examine associations with cognitive impairment. Results: Among participants (mean age: 65.34 ± 6.40 years, 52% female), cognitive impairment was identified in 63.9%. In males, higher VAI was associated with impairment in the FAST test [Odds ratio (OR): 1.601, 95% CI: 1.164–2.203; P = 0.004)], while in females, VAI in the second quartile was linked to lower odds of impairment (OR = 0.583, 95% CI: 0.341–0.997, p = 0.049). No consistent associations were observed for TyG or LAP. Conclusion: Although some sex- and test-specific associations were observed, particularly with VAI, these findings highlight the complex and multifactorial association between metabolic health and cognitive function. This implies that factors beyond insulin resistance may contribute. Further longitudinal research is needed to clarify these relationships.

Background Despite the availability of diagnostic tools and effective treatments for osteoporosis (OP), many women do not receive adequate care. This study seeks to explore the diagnosis and treatment gaps for OP among postmenopausal women. Methods This cross-sectional study was conducted among Iranian women aged 50 and older selected through simple random sampling. The dependent variables were diagnosis gap and treatment gap of osteoporosis. Covariates included demographic, socioeconomic, clinical, and FRAX-related factors. After FRAX screening, participants completed a structured questionnaire. Multiple logistic regression was performed, and model selection was conducted using a backward stepwise method. Model performance was assessed using goodness-of-fit tests and AUC. Analyses were performed in Stata 14. Results A total of 998 women (mean age 64.6 ± 10.4 years) were included in this study. Of these, 346/998 (34.67%) were classified as being at high risk of fragility fractures based on FRAX. Among the 346 high-risk women, the osteoporosis diagnosis gap was 237/346 (68.49%), and the overall treatment gap was 248/346 (71.67%). Of 109 women diagnosed with osteoporosis and considered at high risk for fragility fractures, 11/109 (10.09%) remained untreated. Elementary education (OR = 4.80, 95% CI: 1.65–13.99, P = 0.004) and awareness of osteoporosis and its complications (OR = 6.03, 95% CI: 3.38–10.73, P < 0.001) were associated with higher odds of osteoporosis diagnosis, whereas rural residence (OR = 0.52, 95% CI: 0.30–0.91, P = 0.022) was associated with lower odds of being undiagnosed, indicating a protective effect against the diagnosis gap. No other variables, including age, BMI, and comorbidities, were significant. No predictors were associated with the treatment gap. The model showed good fit (AUC = 0.78, Hosmer–Lemeshow P = 0.45) and no multicollinearity was detected. Conclusions Significant gaps exist in the diagnosis and treatment of osteoporosis among Iranian women aged 50 years and older, with diagnosis and treatment gaps of 68.49% and 71.67%, respectively. Addressing these gaps requires strategies to increase patient and provider awareness, enhance primary care screening, facilitate BMD referrals, and reduce barriers to care, particularly in rural areas.

Hypertension is a major global health concern associated with chronic conditions, particularly in aged adults. Managing these conditions often requires mul