Printable View
Note here that there is argument as I have made earlier for lower test levels than 700 to 900 which is above average level .. I have made this earlier in the thread so won't repeat myself . I would imagine one can use this ratio with the lower optimal levels mentioned earlier.Quote:
Originally Posted by Pegasus
I’m an older guy and my TT ranges from 290 to 310. I do not seem to have a problem with libido; however, I do suffer with ED brought on by diabetes and I have difficulty having an orgasm.
Each guy will have different ideal levels but I have set out the sort of range I believe it will fall in.
Maybe the ratio is more important still. I have read reports of 700 test and 10 estradiol being absolutely dead libido wise, it would be interesting to see how would rank sexuality wise test/est: 700:10, 350:10, 700:20, 350:20. From roiders it's quite unanimous that testosterones libido effects tops around upper reference range and too low estrogen kills sexuality. While the best is very likely 700:20, 700:10 maybe isn't any better than 350:10.Quote:
Originally Posted by Pegasus
I understand the ideal ratio is circa 35 to one . Of course that assumes the test is in reasonable shape to start with ,Quote:
Originally Posted by Venti
Like wise moderate test with too high estrogen kills the penis .
As per post 171.
Population-Level Decline in Serum Testosterone Levels in American Men
Thomas G. Travison, Andre B. Araujo, Amy B. O’Donnell, Varant Kupelian, John B. McKinlay
The Journal of Clinical Endocrinology & Metabolism, Volume 92, Issue 1, January 2007, Pages 196–202, https://doi.org/10.1210/jc.2006-1375
Published:
01 January 2007
Article history
- https://oup.silverchair-cdn.com/UI/app/svg/pdf.svgPDF
- Split View
- Cite
- Permissions Icon Permissions
- Share
Abstract
Context: Age-specific estimates of mean testosterone (T) concentrations appear to vary by year of observation and by birth cohort, and estimates of longitudinal declines in T typically outstrip cross-sectional decreases. These observations motivate a hypothesis of a population-level decrease in T over calendar time, independent of chronological aging.
Objective: The goal of this study was to establish the magnitude of population-level changes in serum T concentrations and the degree to which they are explained by secular changes in relative weight and other factors.
Design: We describe a prospective cohort study of health and endocrine functioning in randomly selected men of age 45–79 yr. We provide three data collection waves: baseline (T1: 1987–1999) and two follow-ups (T2: 1995–1997, T3: 2002–2004).
Setting: This was an observational study of randomly selected men residing in greater Boston, Massachusetts.
Participants: Data obtained from 1374, 906, and 489 men at T1, T2, and T3, respectively, totaling 2769 observations taken on 1532 men.
Main Outcome Measures: The main outcome measures were serum total T and calculated bioavailable T.
Results: We observe a substantial age-independent decline in T that does not appear to be attributable to observed changes in explanatory factors, including health and lifestyle characteristics such as smoking and obesity. The estimated population-level declines are greater in magnitude than the cross-sectional declines in T typically associated with age.
Conclusions: These results indicate that recent years have seen a substantial, and as yet unrecognized, age-independent population-level decrease in T in American men, potentially attributable to birth cohort differences or to health or environmental effects not captured in observed data.
Issue Section:
Endocrine Care
CONSIDERABLE LOSS OF serum testosterone (T) is thought to be a feature of male chronological aging (1–9). Low-serum T has been associated with numerous age-related adverse health conditions including abdominal obesity, diabetes, and prediabetic states (such as insulin resistance, impaired glucose tolerance, and metabolic syndrome), dyslipidemia, low bone and muscle mass, impaired sexual function, depressed mood, frailty, and decreased quality of life (10–12). T decline across the life span therefore represents an issue of great concern for public health, but large studies of within-person decreases in T are rare.
A previous analysis of baseline (T1: 1987–1989) and initial follow-up (T2: 1995–1997) data from the Massachusetts Male Aging Study (MMAS) indicated that the mean longitudinal (within-subject) decline in serum total T (TT) per year of aging was more than twice the baseline cross-sectional decrease in mean TT per year of age (13). Qualitative comparisons of other existing studies likewise indicate that longitudinal decline within subjects is generally of greater magnitude than corresponding cross-sectional trends. We have hypothesized (13) that this disparity may be attributable to rapid intrasubject declines in health among subjects enrolled in longitudinal studies. A competing hypothesis, however, asserts that a population-level decline in T concentrations confounds cross-sectional and longitudinal estimates of T decline with age. A population-level decrease in serum T levels could accelerate the longitudinal declines in T concentrations typically associated with subjects’ aging and compress cross-sectional decreases associated with age. Completion of the latest follow-up wave of MMAS data collection (T3: 2002–2004) allows us to investigate formally the possibility of an age-independent decline in serum T levels with calendar time.
To our knowledge, there exist no extensive published studies of changes in the age-matched distribution of T over time, but a population-level decline in serum T concentrations would be consistent with evidence of secular decreases in male fertility and sperm count (14, 15). In this analysis, we estimated differences in serum total testosterone and calculated bioavailable T (BT) concentrations obtained from individuals of like age observed at different times (e.g. comparing TT in men who were 65 yr old in 1988 to those in comparable men who were 65 yr old in 2003). Our working hypothesis was that age-independent differences would be attributable to population-level changes in health and lifestyle observable during the nearly 20 yr of study follow-up.
Subjects and Methods
The MMAS is a prospective cohort study of men’s health and endocrine function. Its design and prior results are described elsewhere (1, 5, 13, 16). Briefly, from a randomly chosen sample of 1709 men living in and around Boston, blood samples and interview data were obtained during in-home visits by trained staff, with data collection comprising a baseline (T1) and two follow-up (T2, T3) waves. All study activities, including informed consent protocol, were approved by the Institutional Review Board of the New England Research Institutes.
T concentrations are subject to systematic variation due to components of study design (17–19). Accordingly, the MMAS took steps to minimize design bias. To counteract the effects of episodic secretion of hormones, two samples were obtained at each visit and pooled in equal aliquots at the time of assay. To control the effects of diurnal variation in hormone concentrations (20), samples were obtained within 4 h of subjects’ waking. Blood was kept in an ice-cooled container for transport and centrifuged within 6 h. Serum was stored in 5-ml scintillation vials at −20 C, shipped to the laboratory within 1 wk by same-day courier, and stored at −70 C until the time of assay. All hormone values were obtained by a single technician at the Endocrine Laboratory, University of Massachusetts Medical Center, under the direction of Christopher Longcope, M.D. TT concentrations were obtained by RIA (Diagnostic Products Corp., Los Angeles, CA). T1 assays were performed in 1994, whereas T2 and T3 samples were assayed soon after in-home visits. TT inter-assay coefficients of variation were 8.0, 9.0, and 8.3 at T1, T2, and T3, respectively. TT concentrations obtained in the MMAS fall near the center of the distribution of concentrations obtained in other major epidemiologic studies (16), and quality-control testing indicated negligible change in concentrations between T1 and T2 due either to sample storage or assay drift (5).
SHBG was measured using RIA kits at T1 and T2, and at T3 by chemiluminescent enzyme immunometric assay using the Diagnostics Products Corp. (Los Angeles, CA) Immulite technology. SHBG interassay coefficients of variation were 10.9, 7.9, and 3.0% at T1, T2, and T3, respectively. BT was calculated using the mass action equations described by Södergard et al. (21), with association constants taken from Vermeulen et al. (22).
Covariate data
Demographic characteristics (age, education, income, marital status), health conditions (cancers, diabetes, heart disease, hypertension, and ulcer), self-assessed general health (a five-point ordinal scale), and smoking and daily alcohol consumption (23) were obtained via self-report. Self-reported diagnoses of prostate cancer were supplemented with examination of available medical records. Height, weight, and waist and hip circumferences were obtained using methods developed for large-scale epidemiological field work (24). Body mass index and waist-to-hip ratio were derived by calculation. A comprehensive inventory of all prescription medications used by subjects was obtained. Daily caloric intake was measured using the Willett 1-yr food frequency questionnaire (25). Physical activity and energy expenditure were derived from subjects’ 7-d recall of duration and frequency of their activities (26). Depressive symptoms were measured using the Center for Epidemiologic Studies–Depression scale (27).
Analysis sample
To enhance comparability of age distributions across study waves and to allow for analyses of T concentrations by subjects’ birth cohorts, data were restricted to observations obtained on men of age 45–79 yr born between 1916 and 1945, inclusive. This yielded potential samples of 1399, 975, and 579 observations at T1, T2, and T3, respectively. Of these, we excluded all observations on the seven men who had T1 serum total T less than 100 ng/dl (3.5 nmol/liter), and two outlying observations with total T more than 1200 ng/dl (41.6 nmol/liter). One hundred twenty-six observations were excluded because they were taken on subjects who, before the relevant study wave, had a diagnosis of prostate cancer, for which treatment via hormone suppression therapy could not be ruled out. An additional 44 observations were excluded because subjects lacked complete health data. This yielded samples of 1374, 906, and 489 observations at T1, T2, and T3, respectively, totaling 2769 observations taken on 1532 men.
Statistical analysis
Exploratory analyses were conducted to assess the functional form of associations. We used mixed-effects linear regression (28) with random subject-level intercepts and slopes to estimate trends and test hypotheses. Hormone concentrations were log (base e) transformed to remove any effects of the mild skew in the data. For a covariate with associated regression estimate β*, we approximated the corresponding percent change in mean hormone concentrations using the quantity 100 × (eβ*-1). Results were considered statistically significant if null hypotheses could be rejected at the 0.05 level. The significance of effects was evaluated using Wald and likelihood ratio tests. Confounders were used in multivariate models if they had considerable theoretical importance or were significantly associated with T concentrations in the presence of other predictors. All confounders were allowed to vary with time and were treated as internal time-dependent covariates (29).
Results
A description of the analysis sample is given in Table 1. Median baseline age was 58 yr, with interquartile range 52–64 yr. Seven hundred nineteen (52%) subjects reported at least one chronic illness, 340 (25%) were current smokers, 296 (22%) were obese (body mass index ≥ 30), and 300 (22%) reported use of at least three prescription medications. Over the course of study follow-up, we observed marked increases in the proportion of subjects reporting at least one chronic illness or who were overweight or obese, as well as in the number of medications being used by subjects; there were dramatic decreases in the proportion of subjects who were current smokers or who were employed.
TABLE 1.Descriptive statistics by MMAS study wave, mean (SD), or count (%)
T1 (1987–1989) (n = 1374) T2 (1995–1997) (n = 906) T3 (2002–2004) (n = 489) Age (yr) 57.7 (7.2) 63.2 (7.8) 67.3 (6.5) Chronic illness Nonprostate cancers 89 (6%) 124 (14%) 85 (17%) Diabetes 120 (9%) 80 (9%) 62 (13%) Heart disease 196 (14%) 155 (17%) 114 (23%) Hypertension 449 (33%) 340 (38%) 248 (51%) Ulcer 146 (11%) 117 (13%) 64 (13%) Any 719 (52%) 545 (60%) 349 (71%) Depressive symptoms (CES-D ≥ 16) 149 (11%) 96 (11%) 43 (9%) Self-assessed general health Excellent 417 (30%) 280 (31%) 127 (26%) Very good 475 (35%) 336 (37%) 190 (39%) Good 360 (26%) 219 (24%) 110 (27%) Fair/poor 120 (9%) 71 (8%) 42 (9%) Prescription medications 0 517 (38%) 196 (22%) 0 (0%) 1–2 557 (41%) 351 (39%) 170 (37%) 3–5 252 (18%) 270 (30%) 178 (38%) 6+ 48 (3%) 89 (10%) 116 (25%) Education <High school 173 (13%) 83 (9%) 34 (7%) High school graduate 263 (19%) 137 (15%) 81 (17%) >High school 938 (68%) 680 (76%) 374 (76%) Marital status Single/never married 108 (8%) 63 (7%) 40 (8%) Married 1044 (76%) 701 (77%) 367 (75%) Divorced/separated 171 (12%) 97 (11%) 55 (11%) Widowed 51 (4%) 45 (5%) 27 (5%) Household income <$40,000/yr 546 (41%) 271 (31%) 122 (26%) $40,000–$79,000/yr 530 (40%) 299 (34%) 153 (32%) >$80,000/yr 250 (19%) 302 (35%) 199 (42%) Currently employed 1032 (75%) 565 (62%) 257 (53%) Weight and body shape Body mass index (kg/m2) 27.4 (4.4) 27.6 (4.4) 28.3 (4.8) Waist-to-hip ratio 0.95 (0.06) 0.96 (0.06) 0.97 (0.06) Cigarette smoking 340 (25%) 118 (13%) 45 (9%) Dietary intake Total kcal/d 2069 (817) 2006 (720) 1911 (743) Animal fat (g/day) 40.3 (22) 36.6 (19) 38.0 (20) Sedentary activity levels 488 (36%) 285 (31%) 139 (28%)
Table 2 presents descriptive statistics for age and T concentrations at all study waves. Median TT at baseline was 501 ng/dl (17.4 nmol/liter), with interquartile range 392–614 ng/dl (13.6–21.3 nmol/liter); the corresponding values at T3 were 391 ng/dl (13.6 nmol/liter) and 310–507 ng/dl (10.7–17.6 nmol/liter). Among subjects on whom follow-up data could be obtained, the median lag time between observations at T1 and T2 was 8.8 yr, and between T2 and T3 was 6.4 yr.
TABLE 2.Total and calculated bioavailable T concentrations, by study wave and corresponding age range
Study wave Observation years Age range (yr) n TT (ng/dl)a Bioavailable T (ng/dl)a Median Interquartile range Median Interquartile range T1 1987–89 45–71 1383 501 392–614 237 179–294 T2 1995–97 50–80 955 435 350–537 188 150–234 T3 2002–04 57–80 568 391 310–507 130 101–163
aMay be converted to nmol/liter via multiplication by 0.03467.
Exploratory analyses
We used graphical displays to assess three interrelated quantities: first, the cross-sectional association between T concentrations and age at any study wave; second, the longitudinal decline of T over time associated with subjects’ aging; and third, the age-matched difference between, for instance, mean T concentrations obtained from 65-yr-old men in 1988 and concentrations obtained from 65-yr-old men in 2003 (equivalently, we sought to compare T concentrations obtained in 1988 from men born circa 1923 to concentrations obtained in 2003 from men born circa 1938). A depiction of mean TT concentrations is given in Fig. 1, which displays nonparametric locally weighted estimates of TT by age separately for each study wave. The negative slopes of the wave-specific fits correspond to the relatively modest cross-sectional decline of mean TT with age. The age-matched difference by time (denoted by the vertical distance between the fitted curves in overlapping age ranges) is likewise evident. The data suggest that the cross-sectional decline of TT within T1 is smaller than the age-matched difference between concentrations taken at T2 vs. T1, which are separated by only approximately 9 yr in time; simple linear regression estimates indicate cross-sectional TT decreases of 17 and 20 ng/dl (0.6 and 0.7 nmol/liter) per 10 yr of age at T1 and T2, respectively, whereas the mean difference between subjects age 65 at T1 vs. subjects age 65 at T2 is approximately 50 ng/dl (1.7 nmol/liter).
There are many things that can cause a drop in testosterone levels in men such as Cancer treatments and opioids
https://r.search.yahoo.com/_ylt=Awr9...J.EVdUmVsTbQ4-
Diet can also have an effect on testosterone levels. Many will be led to believe that a diet high in red meat can lower test levels but this is not true. Red meat is good for men and will help to raise testosterone levels.
Always remember that what you read on the net, in many cases has been written by those that are pushing the covid-19 narrative and no sign of voter fraud.
https://r.search.yahoo.com/_ylt=Awr9...AC0tsrkM4TNmo-
Problems associated with abnormally high testosterone levels in men include:
- Low sperm counts, shrinking of the testicles and impotence (seems odd, doesn't it?)
- Heart muscle damage and increased risk of heart attack
- Prostate enlargement with difficulty urinating
- Liver disease
- Acne
- Fluid retention with swelling of the legs and feet
- Weight gain, perhaps related in part to increased appetite
- High blood pressure and cholesterol
- Insomnia
- Headaches
- Increased muscle mass
- Increased risk of blood clots
- Stunted growth in adolescents
- Uncharacteristically aggressive behavior (although not well studied or clearly proven)
- Mood swings, euphoria, irritability, impaired judgment, delusions
Also as stated earlier risk taking behaviour.Quote:
Originally Posted by Pegasus