Perceived age is associated with bone status in women aged 25-93 years

Clues for Facial Perceived Age: Exercise, Sun Protection, Photoaging, and Anthropometric Properties: An Observational Cross-Sectional Study

BACKGROUND AND OBJECTIVES

The effect of environmental and genetic factors on the aging process is widely acknowledged. Yet, the extent to which each factor decisively contributes to the perception of looking younger or older remains a subject of debate. This study seeks to identify the factors linked to the perceived age among Turkish women.

PATIENTS AND METHODS

Ten assessors scored the perceived ages of 250 female patients based on facial photographs. The study aimed to assess the impact of environmental factors and anthropometric measurements on the perception of aging. A comprehensive analysis involved conducting 9 perioral and 6 periorbital anthropometric measurements on all study participants.

RESULTS

Exercise (p = .001), mild photodamage (stage 1-2) (p = .001), consistent sunscreen use (p = .001), the length of the palpebral fissure (p = .043), and the height of the upper vermilion (p = .019) demonstrated significant associations with a more youthful appearance.

CONCLUSION

Environmental factors, including exercise, photoprotection, sunscreen use, and anthropometric measurements such as palpebral fissure length and upper vermilion height, play a significant role in contributing to a more youthful appearance.

Applications of artificial intelligence in dementia

The recent evolution of artificial intelligence (AI) can be considered life-changing. In particular, there is great interest in emerging hot topics in AI such as image classification and natural language processing. Our world has been revolutionized by convolutional neural networks and transformer for image classification and natural language processing, respectively. Moreover, these techniques can be used in the field of dementia. We introduce some applications of AI systems for treating and diagnosing dementia, including image-classification AI for recognizing facial features associated with dementia, image-classification AI for classifying leukoaraiosis in MRI images, object-detection AI for detecting microbleeding in MRI images, object-detection AI for support care, natural language-processing AI for detecting dementia within conversations, and natural language-processing AI for chatbots. Such AI technologies can significantly transform the future of dementia diagnosis and treatment. Geriatr Gerontol Int 2024; 24: 25-30.

Younger facial looks are associate with a lower likelihood of several age-related morbidities in the middle-aged to elderly

BACKGROUND

Looking older for one's chronological age is associated with a higher mortality rate. Yet it remains unclear how perceived facial age relates to morbidity and the degree to which facial ageing reflects systemic ageing of the human body.

OBJECTIVES

To investigate the association between ΔPA and age-related morbidities of different organ systems, where ΔPA represents the difference between perceived age (PA) and chronological age.

METHODS

We performed a cross-sectional analysis on data from the Rotterdam Study, a population-based cohort study in the Netherlands. High-resolution facial photographs of 2679 men and women aged 51.5-87.8 years of European descent were used to assess PA. PA was estimated and scored in 5-year categories using these photographs by a panel of men and women who were blinded for chronological age and medical history. A linear mixed model was used to generate the mean PAs. The difference between the mean PA and chronological age was calculated (ΔPA), where a higher (positive) ΔPA means that the person looks younger for their age and a lower (negative) ΔPA that the person looks older. ΔPA was tested as a continuous variable for association with ageing-related morbidities including cardiovascular, pulmonary, ophthalmological, neurocognitive, renal, skeletal and auditory morbidities in separate regression analyses, adjusted for age and sex (model 1) and additionally for body mass index, smoking and sun exposure (model 2).

RESULTS

We observed 5-year higher ΔPA (i.e. looking younger by 5 years for one's age) to be associated with less osteoporosis [odds ratio (OR) 0.76, 95% confidence interval (CI) 0.62-0.93], less chronic obstructive pulmonary disease (OR 0.85, 95% CI 0.77-0.95), less age-related hearing loss (model 2; B = -0.76, 95% CI -1.35 to -0.17) and fewer cataracts (OR 0.84, 95% CI 0.73-0.97), but with better global cognitive functioning (g-factor; model 2; B = 0.07, 95% CI 0.04-0.10).

CONCLUSIONS

PA is associated with multiple morbidities and better cognitive function, suggesting that systemic ageing and cognitive ageing are, to an extent, externally visible in the human face.

Perceived Age as a Mortality and Comorbidity Predictor: A Systematic Review

INTRODUCTION

Perceived age is defined as how old a person looks to external evaluators. It reflects the underlying biological age, which is a measure based on physical and physiological parameters reflecting a person's aging process more accurately than chronological age. People with a higher biological age have shorter lives compared to those with a lower biological age with the same chronological age. Our review aims to find whether increased perceived age is a risk factor for overall mortality risk or comorbidities.

METHODS

A literature search of three databases was conducted following the PRISMA guidelines for studies analyzing perceived age or isolated facial characteristics of old age and their relationship to mortality risk or comorbidity outcomes. Data on the number of patients, type and characteristics of evaluation methods, evaluator characteristics, mean chronologic age, facial characteristics studied, measured outcomes, and study results were collected.

RESULTS

Out of 977 studies, 15 fulfilled the inclusion criteria. These studies found an increase in mortality risk of 6-51% in older-looking people compared to controls (HR 1.06-1.51, p < 0.05). In addition, perceived age and some facial characteristics of old age were also associated with cardiovascular risk and myocardial infarction, cognitive function, bone mineral density, and chronic obstructive pulmonary disease (COPD).

CONCLUSION

Perceived age promises to be a clinically useful predictor of overall mortality and cardiovascular, pulmonary, cognitive, and osseous comorbidities.

LEVEL OF EVIDENCE III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis

BACKGROUND

In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined.

METHODS

To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease.

RESULTS

The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease.

CONCLUSIONS

Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.

Perceived facial age and biochemical indicators of glycemia in adult men and women

Glycemia is linked with one of the key mechanisms underlying the aging process and inter-individual differences in biological age. Previous research showed that glucose level is linked with perceived age in elder individuals. This study aimed to verify if glycemia is related to perceived facial age in healthy adult individuals as interventions in younger and healthy cohorts are crucial for preventing the onset of age-related diseases. The study sample consisted of 116 healthy men of mean age 35.53 ± 3.54 years (29.95–44.29) and 163 healthy women of mean age 28.38 ± 2.40 (24.25–34.17) years. Glycemia was evaluated by fasting glucose, insulin, HOMA-IR, and glycated hemoglobin level. BMI, facial sexual dimorphism, estradiol, testosterone, and hsCRP levels were controlled. Perceived age was evaluated based on standardized facial photos in an online survey. Additionally perceived facial aging was calculated as a difference between perceived age and chronological age. No relationship between the levels of biochemical indicators of glycemia and perceived facial age or aging was found both in men and women, also when controlled for possible confounders. This study shows that perceived facial age in adult individuals is rather linked with body adiposity of sexual dimorphism but not with glycemic markers.

Screening of Alzheimer's disease by facial complexion using artificial intelligence

Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia is yet to be discovered. This study aimed to examine whether artificial intelligence (AI) could distinguish between the faces of people with cognitive impairment and those without dementia.121 patients with cognitive impairment and 117 cognitively sound participants were recruited for the study. 5 deep learning models with 2 optimizers were tested. The binary differentiation of dementia / non-dementia facial image was expressed as a “Face AI score”. Xception with Adam was the model that showed the best performance. Overall sensitivity, specificity, and accuracy by the Xception AI system and AUC of the ROC curve were 87.31%, 94.57%, 92.56%, and 0.9717, respectively. Close and significant correlations were found between Face AI score and MMSE (r = −0.599, p < 0.0001). Significant correlation between Face AI score and chronological age was also found (r = 0.321, p < 0.0001). However, MMSE score showed significantly stronger correlation with Face AI score than chronological age (p < 0.0001). The study showed that deep learning programs such as Xception have the ability to differentiate the faces of patients with mild dementia from that of patients without dementia, paving the way for future studies into the development of a facial biomarker for dementia.

Cognitive function has a stronger correlation with perceived age than with chronological age

Aim

The perceived age of older adults, as measured by their facial appearance, has been shown to be a robust biomarker of aging predictive of survival, telomere length and DNA methylation, and reportedly correlates with carotid atherosclerosis and bone status. This study aimed to determine whether metrics of dementia, including general cognition, vitality, depressive state and self‐supportability, have stronger correlations with perceived age than with chronological age.

Methods

This study included 124 patients who were admitted to the Department of Geriatric Medicine, The University of Tokyo Hospital, on account of being suspected of cognitive decline. The Mini‐Mental State Examination, Vitality Index, Geriatric Depression Scale‐15, instrumental activities of daily living and Barthel Index were carried out. Five experienced geriatricians and five experienced clinical psychologists determined the perceived age of participants based on photographs.

Results

The average values of the 10 raters showed excellent reliability (intraclass correlation coefficient (3, 10) = 0.941). Steiger's test revealed that perceived age showed a significantly better correlation with the Mini‐Mental State Examination (female) and Vitality Index (total, female) than did chronological age, but not with Geriatric Depression Scale‐15, instrumental activities of daily living or the Barthel Index.

Conclusions

Perceived age was shown to be a reliable biomarker for cognitive assessment. Geriatr Gerontol Int 2020; 20: 779–784.

Association between renal function and bone mineral density in healthy postmenopausal Chinese women

BACKGROUND

The relationship between renal function and bone mineral density (BMD) is controversial. The aim of this study was to determine the relationship of renal function with BMD and osteoporosis risk in healthy postmenopausal Chinese women.

METHODS

A cross-sectional study was conducted in 776 healthy postmenopausal Chinese women. Dual-energy X-ray absorptiometry was used to measure BMDs. Clinical, demographic, and biochemical data were obtained at the time of image acquisition. Estimated glomerular filtration rate (eGFR) was calculated using a Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.

RESULTS

Women with eGFR levels of at least 90 ml/min/1.73m² had a lower prevalence of osteoporosis compared with women with decreased eGFR levels (60 ml/min/1.73 m² ≤ eGFR < 90.0 ml/min/1.73 m²). BMDs at femoral neck and total hip were significantly lower in the lower eGFR class than the higher class (0.717 ± 0.106 vs 0.744 ± 0.125 g/cm², P < 0.01; 0.796 ± 0.116 vs 0.823 ± 0.129 g/cm², P < 0.01, respectively). eGFR was positively correlated with BMDs at femoral neck and total hip in unadjusted analysis (P < 0.05). After controlling for age, menopausal duration and body mass index (BMI), decreased eGFR was not associated with osteoporosis risk.

CONCLUSIONS

After adjustments for age, menopausal duration and BMI, the decline in renal function was not independently associated with osteoporosis risk in healthy postmenopausal Chinese women.

A variety of processes that affect the perception of skin aging

The purpose of this paper is to discuss the most important factors affecting perceived age. Aging is an unavoidable and irreversible process, but perceived age is a modifiable psychosocial factor. Our exterior has become one of the determinants of our social position, the key to success in our professional and personal lives. Today, we see people through their appearance, we judge after the first impression. With the help of aesthetic medicine, we can change the perceived age, but we cannot stop aging. This article discusses factors affecting the perceived age, such as hair color, skin color, general appearance and environmental factors. Many cannot be avoided, but we can make changes in life to look younger and more well-groomed. Indeed, we can change our habits for the benefit of the whole body, not only for the skin.

DNA methylation age and perceived age in elderly Danish twins

Perceived age is an easily accessible biomarker of aging. Here, we studied its relation to DNA methylation age (DNAm age) as introduced in (Horvath, 2013) in 180 elderly Danish twins. We found perceived age and DNAm age to be associated with chronological age (P=0.04 resp. P=2.2e-10) when correcting for gender, but did not see an association between perceived age and DNAm age (P=0.44). Intrapair-analysis showed that the proportion of pairs where the twin with the highest perceived age also had the highest DNAm age was not different from 0.5 (P=1), and we did not see a trend when dividing pairs according to their difference in perceived age (P=0.36). Hence, intrapair analysis did not reveal links between perceived age and DNAm age. Moreover, none of the 353 CpGs underlying DNAm age was individually associated with perceived age after correction for multiple-testing (P>6e-4, FDR>0.21). Finally, when constructing an epigenetic signature based on these CpGs to predict perceived age, we only found a correlation of 0.18 (95%CI: -0.06 to 0.40) and a mean square error of 13.6 years2 between observed and predicted values in the test dataset, indicating poor predictive strength. Altogether, our results suggest that perceived age and DNAm age capture different aging aspects.

WHOLE BODY AND REGIONAL BONE MINERAL CONTENT AND DENSITY IN WOMEN AGED 20-75 YEARS

BACKGROUND

Dual-energy X-ray absorptiometry (DXA) allows measurement of whole body (WB) and regional bone mineral content (BMC) and density (BMD).

OBJECTIVE

To measure WB and regional bone area, BMC and BMD (arms, legs, ribs and pelvis) in women of different ages.

SUBJECTS AND METHODS

140 women participated (age range 20-75 yrs). Three subgroups were built: 20-44 yr (30 premenopausal women), 45-59 (80 women), and 60-75 (30 women). WB DXA was performed on a Hologic QDR 4500 A bone densitometer (Hologic Inc., Bedford MA). WB BMD T-scores were calculated by using the manufacturer-provided and the NHANES 1999-2004 reference databases, while the WB BMC Z-scores - based on the latter. Statistical analysis was performed on an IBM SPSS Statistics 19.0 for Windows platform (Chicago, IL).

RESULTS

WB BMC and BMD Z-scores were consistently lower than the reference databases showing a difference of about 0.4 - 0.5 SD. The arms, legs and ribs lost more BMC after the age of 50-55, while the pelvis - much earlier. The total decreases in BMC were highest in the pelvis (26.36 %), followed by the arms (16.81 %) and whole body (15.91 %), while the bone area decreased mostly in the pelvis (13.23 %).

CONCLUSION

The age-related declines in regional BMC, bone areas and BMD follow different patterns in appendicular and axial bones.